I spent years as an equity research analyst through the 90s and 2000s—long days, fragmented workflows, and a constant race just to get to the starting line. Before you could think, you had to assemble. Data from everywhere. Notes stitched together. Models built under time pressure. Even at full tilt, coverage capped out at ~10–12 names.

Fast forward to today, and the structure of the day is still the same. What’s changed is how quickly you can move through it—and increasingly, whether you need to do it at all.

In a series of posts, I'll be reliving my days as a publishing analyst - but using viaNexus marketplace data and the modern tools available to all of us. I'll simplify a bit because I was also reading some of the content in Spanish!

6:00–7:00 AM — The Morning Brief, Reimagined

The first task of the day was always simple: Take stock.

• What happened overnight?
• Where are my names?
• What matters today and for the next few days?

That hour is now collapsing into minutes. Using Claude + Skills + viaNexus and partner data, we’ve rebuilt the morning brief from the ground up.

At its core, a Skill is a structured, repeatable workflow—essentially a highly tuned prompt that knows:

• What to pull (prices, news, events, estimates)
• Where to pull it from (permissioned, normalized datasets via viaNexus)
• How to present it (structured, interactive output)

A typical Morning Brief Skill pulls together data from multiple sources, and where necessary joins and reasons over that data:

• Stock and sector performance across your coverage
• Premium news (e.g., MT Newswires)
• Company context and prior earnings summaries
• Upcoming earnings with confirmed call times and calendar integration (e.g., Aiera, ExtractAlpha)

In this case, I had Claude write the skill for me - at least to do a first cut. Then I edited it directly once I had a good first stab from Claude. All in all took about 15 minutes! Then it's just "rinse-repeat".

Type “Morning Brief” and within 5 minutes you get a fully interactive dashboard—generated on demand, inside your context window. The video below shows the 5 minutes speeding up!

Not a PDF. Not a static report. A live, structured, filterable workspace.

Under the hood, it’s dynamically:

• Reading your coverage universe
• Pulling normalized data across datasets
• Joining and enriching in real time
• Rendering signals, summaries, and forward-looking workflows

It’s effectively rebuilding the institutional morning process—without the manual stitching.

Nothing special in that - I can just use Claude or OpenAI and give it access to the web. Let it scrape away. Well - aside from the fact that data is all over the place on the web, not normalized and will therefor requires a ton of reasoning to figure it out - if you are a professional that scraped data is also not licensed for professional use.... which you legal team won't appreciate!

The Breakthrough… and the Problem

This is where things get interesting. Because while this works—it works too literally. Every time you run the Skill:

• The data is pulled through the MCP protocol (vs a fast API)
• The same joins are re-reasoned (not a big deal for viaNexus data since its so standardized)
• The same interface is rebuilt from scratch (well its mainly the same)
• Multiple MCP calls fire in sequence

It’s elegant. But it’s also Slow, Expensive, and Wasteful. You’re rebuilding the same application… every single morning. At scale, that’s not just inefficient.

Imagine a department of 30 PMs and analysts all building skills the same way.

The right tool for the right job.

Skills are handy short cuts for often repeated prompts, but not idea for more complex queries - unless they are used infrequently. For more complex workflows, they are great at:

• Validating user experience
• Defining data dependencies
• Iterating quickly with AI

But once the workflow is proven, the path forward becomes might be:

• Take the Skill
• Hand it to Claude Code (or your dev stack, or IT team)
• Build a lightweight, persistent application
• Have the app connect directly to viaNexus APIs
• Have the app running continuously if you like.....

Instead of recomputing everything, every time. Same outcome, dramatically faster at a fraction of the cost with no redundant compute.

Why the Data Layer Matters

None of this works without the right data foundation. Skills—and the applications they evolve into—depend on:

• Permissioned, licensed datasets
• Reliable, high-quality sources
• Normalized structures across providers

That’s where viaNexus comes in—bringing together datasets from providers like:

• MT Newswires (for news)
• Aiera (for earnings transcripts and future events)
• ExtractAlpha (for earnings estimates)
• Exchange Data International (for reference data)
• BMLL Technologies (for pre-trade analytics)
• and more coming soon....

All normalized, permissioned, and ready to be consumed - by humans, applications, or agents. Most likely a combination.

The Payoff

The real outcome isn’t just efficiency—it’s capacity. For a publishing analyst:

• 12 covered stocks becomes 20 or more
• Manual prep becomes automated context
• Time shifts back to where it matters:
  • Insight generation
  • Company engagement
  • Differentiated research

And maybe—just maybe - the analyst makes it home in time to tuck the kids into bed!

Getting Started:

While we are getting our own listing sorted, here is how to manually get started with viaNexus, our partners, Claude and Skills:

Go to Claude desktop, or claudu.ai

1) From your profile, go to settings

2) Select "Connectors"

3) Select "Go to customize"

4) hit + and "Add custom connector"

5) Name: whatever you like, the remote MCP server URL is https://vast.blueskyapi.com/vianexus/mcp. Also make sure all the Tools are enabled.

If you don't have an account, select viaNexus, and you'll be guided to sign up for free, 14 day trial.

Don't miss the next exciting episode: 7:00–8:00 AM

At viaNexus, we already distribute as-reported fundamentals and filings, drawn directly from the 10-Q and 10-K filings submitted by public companies. That data is faithful to the source and valuable for traceability.  But it is not sufficient for serious analysis.

As-reported data reflects the inconsistencies of the filings themselves. Companies use different labels, structures, and interpretations for the same accounting concepts. The result is a dataset that mirrors filings accurately—but becomes difficult to compare across companies or across time.

That works for inspection. It breaks down for analysis. Anyone building valuation models, financial dashboards, or AI-driven research workflows quickly encounters the same issue: raw filings are not analytically consistent. If fundamentals were going to be added to viaNexus properly, they would need to be normalized, structured, and delivered in a form that analytical systems—and increasingly AI systems—can rely on.

Doing that properly is far from trivial.

Rather than take shortcuts, we partnered with SavaNet, combining their deep domain expertise in financial reporting with the viaNexus platform for normalization, API delivery, and agent-ready infrastructure.

This approach reflects a broader philosophy. When complex datasets require deep subject-matter expertise, we believe the best solution is to combine domain specialists with modern data infrastructure. You should expect to see more datasets built this way on viaNexus going forward.

 From As-Reported to Analyzable

The distinction between as-reported and normalized data matters.

As-reported fundamentals tell you what a company filed, according to its interpretation of accounting standards, its industry context, and sometimes its narrative choices.

Normalized fundamentals translate those filings into a consistent analytical framework.

Serious financial analysis requires:

• A standard financial statement schema
• Consistent definitions across companies and time
• Stable line items that behave predictably

Only once financial statements are normalized into a coherent structure can you reliably:

• Perform statement-level analysis
• Compare operating performance across peers
• Calculate accounting ratios
• Derive valuation metrics

Normalization isn’t a convenience. It is the foundation that makes analysis possible.

The Illusion of “Standard” XBRL

We began by examining the obvious starting point: U.S. filings.

In theory, XBRL provides a standardized language for financial reporting. In practice, it behaves more like a flexible taxonomy framework than a true standard.

Across U.S. filings, there are more than 10,000 XBRL tags currently in use. Companies frequently describe the same economic concept using different tags, structures, or contextual assumptions.

Consider the first line of the income statement:

• us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax (most common)
• us-gaap:SalesRevenueNet (Common for manufacturers or retailers)
• us-gaap:RevenuesNetOfInterestExpense (Used by financial institutions or special structures)
• us-gaap:InterestIncome (Banks often treat interest income as revenue)
• us-gaap:OperatingRevenue (Sometimes used for regulated industries or utilities)

But some companies use custom extensions - that fits their statement wording - eg:

• aapl:NetSales
• tsla:AutomotiveSalesRevenue

These may represent the same economic concept—or slightly different ones—depending on the filer. Multiply that ambiguity across balance sheets, cash flow statements, industry-specific disclosures, and footnotes, and the problem becomes clear:

XBRL does not produce comparable fundamentals out of the box.

For most analytical use cases, the raw output is simply not usable.

There Is No Shortcut Around the Hard Work

SavaNet’s founder, Eric Linder CFA, has worked with regulatory filings and institutional financial data for decades. Early in the evolution of XBRL, he developed IFRICS, a purpose-built financial taxonomy designed to impose consistent structure on real-world filings.

IFRICS is a five-level hierarchy that maps filings into a standardized financial statement framework.

Rather than simply mapping tags, the framework:

• Resolves semantic ambiguity
• Separates economic meaning from filing convention
• Preserves industry nuance while enforcing consistency

Every reported XBRL item is mapped into this hierarchy through a deterministic process. Edge cases are explicitly handled, and granularity is preserved rather than flattened prematurely.

In many competing datasets, a significant number of items ultimately end up grouped into generic “Other” categories due to insufficient taxonomy depth. The IFRICS approach avoids that outcome by maintaining detailed structural mappings across the hierarchy.

To validate the framework, we tested it rigorously across the Russell 1000, iterating through thousands of disclosures until the dataset formed a consistent and internally coherent structure.

It is meticulous work—but it is the only reliable way to build a truly analyzable fundamentals dataset.

Once the framework is established, however, the methodology scales efficiently across the broader equity universe.

Deterministic by Design

One point is worth emphasizing.  The normalization process does not rely on large language models or manual interpretation.

LLMs are probabilistic by nature. Human interpretation introduces judgment drift over time. Neither approach is well suited for producing datasets that must remain consistent across decades of filings, thousands of companies, and repeated reporting cycles.

Instead, the process is:

• Rule-driven
• Taxonomy-based
• Deterministic and repeatable

Once a filing element is mapped into the schema, it will map the same way tomorrow, next quarter, and five years from now.  This way consistency is not an emergent property, it is designed into the system.

 Why the Schema Matters

The real innovation in the dataset is not just normalization—it is schema design.

The IFRICS taxonomy is deliberately deep, with five levels of abstraction:

• Levels 1–2: Universally comparable financial concepts
• Levels 3–4: Industry-aware financial structure
• Level 5: Filing-level granularity and edge-case detail

This structure allows financial data to be rolled up into consistent, comparable metrics—or drilled into at a very granular level when needed.

Through the viaNexus API, we expose the first two levels of the hierarchy to provide a clean analytical interface. Behind the scenes, the deeper layers preserve the detailed mappings required to maintain structural integrity.

For clients that need full transparency, the deeper mappings can also be made available.

Clean Inputs Create Deterministic Outcomes

Many downstream analytical errors originate upstream in the data.

By resolving ambiguity once—deterministically and at scale—we reduce:

• Reconciliation work for analysts
• Semantic confusion for AI models
• Inconsistent outputs across time and universe

Better inputs lead to more reliable outputs.

This principle becomes even more important as financial workflows increasingly incorporate AI-driven analysis.

Why This Matters for Agentic Workflows

viaNexus is designed to support agentic financial workflows through the vAST services layer.

Agent-driven systems depend on predictable inputs. Ambiguity compounds quickly. Inconsistent datasets lead to inconsistent outputs.

By eliminating ambiguity at the data layer, we give both humans and AI systems a stable analytical foundation.

The reliability of AI systems does not improve simply because the models improve. It improves because the data feeding those systems becomes cleaner, more structured, and more deterministic.

Try It Yourself

You can explore the dataset directly through a free viaNexus account - or dive straight in with a paid tier - tailored for individuals and enterprises.

Sign up at viaNexus.com and browse the catalog.

The dataset currently includes:

• Coverage of more than 3,000 U.S. companies
• More than 250 fields spanning income statement, balance sheet, and cash-flow metrics, along with derived valuation and accounting ratios
• Industrials, banks, and insurance companies (each with their own schemas)
• Five years of financial history, with deeper historical coverage planned

Full documentation is available in the viaNexus API console and here: 

https://console.blueskyapi.com/docs/EDGE/fundamentals/NORMALIZED_FUNDAMENTALS

Fundamentals, Rebuilt as Infrastructure

As-reported fundamentals are useful.

Normalized fundamentals are essential.

This dataset was not designed to sit inside a spreadsheet. It was built as financial data infrastructure—for analysis, valuation, and intelligent systems that require clean, deterministic inputs.

With the right partners and a willingness to do the hard work, what once seemed complex becomes scalable.

And this is just the beginning.  Expect more datasets on viaNexus built using the same model: deep domain expertise paired with modern normalization and delivery infrastructure.

Because the future of financial data is not simply more data, it is better-structured data, delivered as infrastructure.

The Gap Nobody Solved

For the past two years, everyone has talked about AI agents in finance. Here's the truth: they haven't truly worked.

Not because the AI wasn't capable — but because the infrastructure wasn't ready. Data lived behind manual credentialing. Payments required human approval. Every time an agent needed something new, the workflow stopped and waited. These weren't autonomous agents. They were assistants waiting for permission.

Closing that gap requires three things working in concert: data, programmable payments, and an open workspace that doesn't make you reinvent the wheel. No single firm can own all of that. But when the right pieces connect, the loop closes.

What viaNexus Brings to the Stack

viaNexus is a producer-consumer marketplace powered by universe-based pricing. 40+ datasets from providers like MT Newswires, Aiera, ExtractAlpha, and SavaNet, delivered through high-performance APIs and an agentic services layer called vAST.

vAST handles agent-to-data authentication autonomously. The agent determines what it needs, accesses the right datasets, and keeps operating — no credential re-entry, no manual approvals, no broken workflows. When paired with a programmable payments layer, agents can pay for data in real time as they consume it. When deployed into an open workspace, they reason across live data rather than just retrieve it.

The result: user asks a question, viaNexus delivers the data, billing is handled automatically, the agent returns the answer. One seamless workflow with whatever stack you're building in.

What It Looks Like in Practice

To make this concrete: we connected three pieces that each bring something distinct to the workflow. The demos below came out of a conversation over coffee, not a deep integration project or a formal paid partnership. Here's what we used:

• viaNexus — a producer-consumer data marketplace delivering 40+ datasets from providers like MT Newswires, Aiera, ExtractAlpha, and SavaNet through a single unified API and agentic services layer.
• OpenBB — an open, programmable financial workspace that renders viaNexus data as live charts, tables, and dashboards, and provides the environment for deploying AI agents.
• Paygentic — a programmable payments platform built for agentic workflows, handling per-action billing in real time as the agent consumes data.

If this is what's possible in an afternoon, imagine what you could build with a week. 

Connect a viaNexus token and you're immediately looking at rendered charts, live tables, and interactive dashboards — not raw JSON. Real-time quotes render as live charts. Earnings and Events Calendars surface confirmed dates, webcast links, and transcripts. TrueBeats EPS forecasts from ExtractAlpha sit alongside prior quarter actuals from SavaNet. MT Newswires streams real-time headlines filtered by ticker.

The result adapts to whoever is using it. An investor builds a morning portfolio dashboard. A founder shapes it into a monetized research product. An analyst collapses multiple tabs and spreadsheets into one clean interface. Same data layer, different application.

The workflow is straightforward. Open OpenBB, drop in your viaNexus endpoint and token, and the data is live. From there the agent handles the rest — querying datasets, building widgets, and reasoning across them in response to natural language prompts. When you need to top up, ask the agent directly in the workspace, follow the Paygentic link, add funds, and keep going. The whole thing stays in one place, and the agent never stops.

The Agent Is Just One Example

The viaNexus financial agent — built on the viaNexus Agent SDK — is a useful illustration of what's possible on top of this infrastructure. It's not the product. It's a demonstration of what the data layer enables.

It reasons across widgets rather than just reading them. Ask it to summarize weekly performance, compare consensus expectations to last quarter's actuals, or surface estimate revisions ahead of earnings. It can create widgets, analyze existing ones, and pull from the full viaNexus catalog to deliver what would otherwise take hours of manual work.

More importantly, it can watch markets for you. Tell it to alert you when anything in your portfolio forms a death cross, or moves more than X% in a day — and it sends a full data payload to your email the moment that condition is met. The data watches itself. That's not a feature. That's a different way of working.

Every viaNexus subscription includes core datasets: 15-minute delayed pricing, filings, and basic fundamentals. Edge datasets — premium news, transcripts, expanded fundamentals — layer in seamlessly, and the agent incorporates them immediately once enabled.

Built for Anyone With an Idea Worth Building

This doesn't require a large engineering team. A solo developer, a three-person startup, a buy-side analyst who has never touched a data pipeline — everyone gets access to the same institutional-grade layer at a fraction of the historical cost.

viaNexus is the data layer. Plug it into your own app, build your own agent, wire it into an existing workflow. The API is the same either way.

The barriers are coming down. The only question is what you'll build when it happens.

Interested in building on viaNexus? We'd love to hear what you're working on — reach out here or connect with us on LinkedIn!

🔗 Sign up for viaNexus

🔗 Get started with openbb.co

🔗 Explore paygentic.io

Think about what goes into a useful earnings application. Your users need to know when calls are happening—not just the date, but the actual time, the dial-in details, the webcast URL. They need context:

What did the company report last quarter? What's the consensus this time? What's the news sentiment heading into the call? And how on earth do they monitor multiple calls simultaneously, maybe even get real-time transcripts as executives speak.

Building that used to mean stitching together data from half a dozen sources. You'd pull filings from EDGAR for confirmed dates. You'd integrate an estimates provider for consensus numbers. You'd add a news feed for sentiment. You'd find a transcript vendor for call content. Each source has its own schema, its own update cadence, its own quirks. By the time you've normalized everything and built the integrations, earnings season is half over.

The Old Model Is Breaking Down

The traditional approach—pick a primary vendor, supplement with a few feeds, build your own normalization layer— that kind of worked when data moved slower and applications were simpler. But the pace has changed. AI-driven workflows need clean, structured data they can reason over. Modern applications need to react in real time. Portfolio managers want to track thirty earnings calls at once, switching between live transcripts and historical performance. The data infrastructure that powered Bloomberg terminals in 2015 isn't built for the applications people are building in 2026.

What's needed is a different model entirely: pre-normalized data from multiple authoritative sources, accessible through a single API layer, designed from the ground up to support the way developers actually build.

How viaNexus Approaches Earnings Data

viaNexus treats earnings season as what it is: a mosaic. No single dataset tells the complete story. What matters is how the pieces can be integrated into user experience, or “agent experiences”.

We provide two earnings calendars because they solve different problems. Our in-house Earnings Calendar is built directly from 8-K filings—the official announcements companies file with the SEC. When a company confirms an earnings date, we capture it. When they haven't filed yet, we use historical patterns to predict the timing with confidence labels. It's authoritative, fast, and designed for both human readers and AI agents who need to know what's confirmed versus what's forecast.

The AIERA Events Calendar complements this with a different lens. Our partner AIERA tracks not just when earnings are scheduled, but the full event lifecycle. You get the webcast URLs, the dial-in details, the actual start times—everything your users need to actually attend the call. And AIERA doesn't stop when the call begins. Through their Real-Time Events Transcripts dataset, you can stream the earnings call as it happens, word by word, giving your users the ability to monitor multiple calls simultaneously or build AI agents that react to what management says in real time.

Here's what a simple API call to the AIERA Events Calendar looks like:

GET /data/edge/events_calendar/AAPL/earnings

That returns the full schedule—past and future—with metadata your application can act on immediately.

For historical context, our Summary Normalized Financials dataset gives you the prior quarter's actual EPS and revenue, standardized across all companies. It's the baseline your users need to understand if this quarter was better or worse. Want to add consensus expectations? The TrueBeats EPS Revenue Forecasts from Extract Alpha provides the street's numbers—what analysts are expecting, what would constitute a beat or a miss.

And then there's the news layer. The MT Newswires Global dataset gives you the narrative context: what's been written about this company in the run-up to earnings? What are analysts focusing on? What are the risks everyone's watching? News isn't just sentiment—it's the questions your users will be asking when they tune into the call.

What You Can Build

In the following  section we demonstrate weaving together several viaNexus products like NEWS from MT NEWSWIRES, EPS from Extract Alpha, Earnings Transcripts from AIERA, to build an Earnings Dashboard in 3-5min.

1. viaNexus Earnings Calendar – Confirmed and predicted earnings dates from 8-K filings
2. Summary Normalized Financials – Last quarter's actual EPS and revenue
3. TrueBeats EPS Revenue Forecasts – This quarter's consensus expectations
4. MT Newswires Global – Recent headlines for earnings context
5. Reported Financials – Direct URLs to SEC filings (10-Ks, 10-Qs, 8-Ks)
6. AIERA Events Calendar – Press release URLs and event metadata

The code is straightforward because the data already fits together. For example, here's what fetching normalized financials looks like: 

GET /data/core/SUMMARY_NORMALIZED_FINANCIALS/MSFT/quarterly?last=4

That gives you four quarters of history in a consistent schema. No custom parsing. No wondering if revenue is in thousands or millions. It just works.

This is the foundation and it's just scratching the surface. From here, you could layer in more sophisticated features. Use AIERA's event metadata to let users join live calls directly—complete with real-time transcripts that stream word-by-word as executives speak. Build portfolio-wide views that surface every earnings call for a watchlist. Add intelligent alerts that trigger when a company beats consensus by a meaningful margin.

The Investment Process Deserves Better Tools

Earnings season is intense. The information flow is relentless. Investors are trying to make sense of dozens of data points in compressed time windows. The tools they use—whether it's a professional terminal, a self-directed trading app, or an AI-powered research assistant—need fast, clean, integrated data.

That's what viaNexus provides. All the raw materials. Pre-normalized. From trusted sources. Accessible through APIs designed for exactly this kind of multi-dataset integration.

Whether you're building an app or agent to support internal users, building a SaaS app to sell into the buy or sell side, or even building something just for you—viaNexus gives you everything you need to ship fast. The data is ready. The documentation shows you exactly how to call it. The examples prove it works.

The infrastructure is ready. What you build is up to you.

🔗 Learn more: https://vianexus.com
📧 Contact us: support@vianexus.com

AI has lowered the barrier to building powerful applications almost to zero.

You can spin up an agent in an afternoon. Wire it to an LLM. Give it a prompt, a tool, a dataset. Suddenly, it sounds intelligent!

This is true whether you’re:

• vibe-coding a personal project at home, or
• building a production-grade “GPT” intended to support traders, analysts, portfolio managers, or risk teams.

But here’s the uncomfortable truth:

Most AI applications fail not because the model is weak — but because the foundation is.

That’s where the viaNexus Agentic Services Technology (vAST) comes in.

The hidden risk: confidence without competence

There’s a well-documented cognitive bias called the Dunning–Kruger effect:
people with limited knowledge tend to overestimate their competence, while true experts are more aware of uncertainty and limits.

AI unintentionally amplifies this effect.

Modern LLMs produce:

• fluent language
• confident reasoning
• polished outputs

But fluency is not understanding.

In complex, regulated domains like finance, the danger isn’t hallucinations in isolation — it’s confident conclusions drawn from weak, misaligned, or unauthorized data, and then acted upon at scale.

A little bit of knowledge has always been dangerous. AI just makes it faster.

What most AI stacks get wrong

Most AI tooling focuses on:

• model selection
• prompt engineering
• orchestration
• UX

Dangerously, less attention is paid to:

• where the data comes from
• whether it’s licensed for the intended use
• how reliable or authoritative it is
• whether it’s normalized and comparable
• what market or regulatory semantics apply

As a result, agents often reason over:

• scraped or synthetic content
• inconsistent identifiers
• mismatched timestamps
• unclear real-time vs delayed status
• data they were never entitled to use in the first place!

The output may look impressive — until it’s scrutinized by a professional, a compliance team, or a regulator! GULP

 vAST: the missing layer for agentic finance

vAST (viaNexus Agentic Services Technology) exists to solve this problem.

Not just by making AI smarter — but by making AI more disciplined, grounded, and professional.

vAST sits between:

• high-quality data sources (our own and from trusted and vetted partners), and
• the agents and applications that consume them

providing the structural guardrails that AI workflows typically lack.

 Curated data, not random inputs

viaNexus curates best-in-class third-party data from:

• exchanges and reference data sources
• news providers
• analytics specialists
• alternative data vendors

This is not a data dump.

We select providers that:

• are authoritative in their domain
• operate under clear licensing frameworks
• meet professional expectations for accuracy, timeliness, and reliability

That data is then normalized and enriched, so agents aren’t reasoning over disconnected fragments.

 Normalization + reference data = shared reality

One of the most common failure modes in AI systems is silent inconsistency:

• different identifiers for the same instrument
• mismatched symbology
• incompatible calendars
• conflicting entity definitions

viaNexus addresses this by combining curated partner content with highly reliable reference and symbology data.

The result:

• a single, coherent view of markets
• consistent identifiers across datasets
• shared semantics that both humans and agents can rely on
• lower "reasoning" costs

This matters far more than most people realize — especially when decisions carry real financial or regulatory consequences.

Entitlements: knowing what an agent is allowed to know

AI systems are very good at overreaching. Just ask ChatGPT to tell you where it fetched its data from - all over the internet - and from sources that have licensed data solely for non-professional use. So if that data shows up in a response from a GPT from inside your firm - then you have created a licensing issue.

vAST enforces entitlement-aware access, ensuring that agents:

• only see data they are licensed to access
• only use data in permitted ways
• respect display vs non-display rules
• inherit the same constraints a human professional would

This does something subtle but important: It prevents agents from hallucinating authority where none exists.

That alone eliminates a major source of overconfidence.

 Domain semantics baked in, not hand-waved

Complex domains like finance are full of distinctions that matter:

• authoritative vs contextual
• primary vs derived
• fact vs opinion
• point-in-time vs revised
• source-specific vs aggregated

These nuances are easy to gloss over — and novices often do. Experts never do.

viaNexus encodes this domain context directly into the data layer, so agents operating through vAST don’t have to infer or guess. They inherit the structure, provenance, and intent of the data automatically.

This is how you avoid outcomes that are technically plausible, but professionally wrong — and why discipline at the data layer matters as much as intelligence at the model layer.

Designed friction is a feature, not a bug

Most AI platforms optimize relentlessly for speed.

vAST is optimized for appropriate hesitation.

That means:

• clear provenance
• visible assumptions
• entitlement checks
• signals when confidence should pause

This mirrors how real professionals work. And it quietly nudges users — human or agent — away from Dunning–Kruger territory and toward real competence.

From vibe-coding to production, without changing foundations

One of the most powerful aspects of vAST is continuity.

The same foundation can support:

• a weekend experiment
• an internal proof of concept
• a regulated, production-grade system

You don’t have to rewrite your architecture when:

• compliance gets involved
• clients ask hard questions
• regulators appear
• the stakes increase

You’ve already built on solid ground.

 The bottom line

AI makes it easy to sound right.
vAST helps ensure you are right — or at least minimally uncertain!

Whether you’re:

• vibe-coding an app at home, or
• building a professional GPT to support finance professionals

you need:

• high-quality, curated data
• normalized and grounded in reliable reference datasets
• delivered with clear provenance
• governed by entitlements
• and embedded with real market semantics

That’s what viaNexus and vAST provides.

In a world racing to make everyone feel like an expert, we’re focused on something harder — and far more valuable: Scaling intelligence without scaling overconfidence. 

Learn more here: https://vianexus.com/vast/

They mark when companies disclose performance, reset expectations, move prices, and trigger analysis across portfolios, models, and news systems. For many workflows, from research and monitoring to AI driven agents, knowing when earnings happen matters just as much as the earnings themselves.

In the United States, those moments are formally captured through earnings related 8 K filings. These filings are the earliest authoritative signal that earnings information has been released. They are not estimates or announcements. They are the regulatory record.

That makes earnings 8 Ks the ground truth for earnings timing.

The challenge is that while earnings 8 Ks are reliable, they are not predictable. Companies rarely announce dates far in advance. Filing behavior varies. Most earnings calendars rely on assumptions or static schedules rather than regulatory evidence.

The viaNexus Earnings Calendar Events system exists to close that gap by learning directly from historical earnings 8 K behavior and turning it into a clear, machine readable schedule of what is likely to happen next.

Turning Regulatory Signals Into Reliable Schedules

Most earnings calendars answer a surface level question:
What is the earnings date?

They do not answer the questions that actually matter.
Is this date predicted or confirmed?
How confident should I be?
What happens when a real filing arrives?
How does the system adapt after the event passes?

For humans, that ambiguity is frustrating. For AI systems, agents, and automation, it is a breaking point. Without clear state and confidence, earnings dates become brittle signals that cannot be safely acted on.

The mission of the Earnings Calendar Events system is simple and strict.

Take authoritative regulatory signals, earnings 8 Ks, and convert them into a living, trustworthy earnings schedule.

That means tracking only real earnings events, not guesses. Learning filing cadence from actual company behavior. Explicitly distinguishing what is predicted from what is confirmed. Updating automatically as new filings arrive. Publishing one clear answer per company at any point in time.

This system does not forecast performance.
It forecasts timing, with transparency.

Everything downstream depends on that clarity.

An Agent First, Predictive Earnings Pipeline

The system begins with an 8 K classification agent. This agent is purpose-built to identify earnings-related 8-K filings only. It filters out all other 8-K event types and ensures that predictions are generated exclusively from filings that actually correspond to earnings releases, not press updates, restructurings, or unrelated disclosures.

Every day, thousands of 8-K filings are published, the majority of which are not earnings-related. The classifier continuously scans incoming filings and selects only those that explicitly correspond to earnings releases, ensuring that downstream predictions are driven solely by true earnings events.

This upfront filtering defines what the system trusts. Noise is removed before it ever reaches prediction logic. Once a filing is classified as earnings related, it flows into the rest of the pipeline as a high signal event.

Classified filings are stored and from there, an ETL process aggregates historical earnings filings by company and analyzes filing cadence using median intervals. Median intervals are deliberately chosen because they are more robust to late filings, amendments, and one off delays.

Based on that history, the system predicts the next plausible earnings filing date and assigns a confidence score that reflects how consistent that behavior has been over time.

Why This Algorithmic Approach Works

Earnings filings are not scheduled events in the traditional sense. They are behavioral signals shaped by company practice, reporting cycles, and regulatory timing. Treating them as fixed calendar dates introduces assumptions that break as soon as behavior changes.

Rather than imposing a schedule, the Earnings Calendar Events system models filing cadence directly. It looks at how a company has actually behaved and treats those intervals as signals rather than rules.

Using medians instead of averages preserves the central tendency of behavior without allowing outliers to distort the result. The confidence score is not a claim of certainty. It is a measure of stability. Companies with consistent filing patterns produce high confidence predictions. Companies with irregular behavior surface that uncertainty explicitly.

Nothing is smoothed away for convenience. Uncertainty is preserved rather than hidden.

Confirmation, State, and Transparency

Predicted and confirmed dates are never mixed.

Each earnings record is always in one of two states.
Predicted, generated by the algorithm.
Confirmed, backed by an actual earnings 8 K filing.

When a new earnings 8 K arrives, the system confirms the actual filing date, clears the prediction, and sets confidence to certainty. After a short expiration window, the system automatically transitions back into prediction mode for the next earnings cycle.

State transitions are driven by direct comparison between newly classified filings and the sources used in the prior prediction, ensuring that confirmation is tied to regulatory evidence rather than timing heuristics.

To ensure transparency, each record also includes the historical earnings 8 K filings used in the calculation. Users can see exactly which dates informed the prediction and how the system arrived at its result. Nothing is treated as a black box.

For downstream systems, this provides clear temporal truth instead of silent ambiguity.

How Customers Use This

Customers use the Earnings Calendar Events system to power earnings aware agents and LLM workflows, trigger alerts ahead of likely earnings windows, drive dashboards with confidence labeled event timing, reduce false positives in event driven strategies, and reliably schedule analysis, reporting, and automation pipelines.

Instead of asking whether a date is real, systems get the answer directly in the data.

From Signals to Schedules

Predicting earnings dates is not about precision for its own sake. It is about context.

The Earnings Calendar Events dataset is designed to work alongside viaNexus filings, news, and event datasets, giving systems a unified, time-aware view of market activity without hiding uncertainty or overfitting behavior.

If you’re building workflows that depend on earnings events, market timing, or event-driven signals, we’d love to connect and hear your thoughts. The Earnings Calendar Events dataset is available to explore with a free trial on viaNexus.

🔗 Learn more: https://vianexus.com/
📧 Contact us: support@vianexus.com

2025 Highlights:

- “Easy Button” Innovation: We unlocked the Easy Button for financial data – automating data onboarding and instantly generating clean, normalized APIs that developers love[3]. No more months-long integrations or mysterious schemas. One click, and complex datasets become plug-and-play resources. (Fun fact: that Easy Button nickname started as an inside joke and ended up defining our product ethos!)

- Content & Partnerships: We onboarded a slew of incredible data partners across news, fundamentals, estimates, indices, and more[4]. From real-time market insights, news, reference data, and corporate actions we brought diverse content onto the platform fast. We also rolled out proprietary datasets – like our exchange-independent real-time feed and LLM-ready financial filings – to fill gaps and showcase what’s possible. For data providers, we made monetization truly turn-key with transparent controls, fair revenue share, and freedom to set pricing[5]. And for consumers, we ditched the old annual contracts – it’s all about fast, fair, on-demand access with no hidden gotchas[6].

- Delivery Breakthrough – Agentic Workflows: Perhaps our coolest leap was going agentic. This year we launched viaNexus Agentic Services Technology (vAST) – an out-of-the-box solution that injects the right data into AI agents and workflows with full entitlement and compliance built-in[7][8]. In other words, we made our platform not just cloud-ready, but AI-ready – turning LLMs from black boxes into powerful front-ends by feeding them the exact data they need, exactly when they need it. No hallucinations, no legal headaches – just autonomous intelligence powered by licensed, structured data. (Yes, we even geeked out with a Matrix reference or two along the way – who doesn’t want to download new data skills on demand?)

- Ecosystem & Community: We didn’t build in a vacuum. viaNexus joined the FinTech Sandbox as a Data & Infrastructure Partner, aligning with a community that helps startups experiment with cutting-edge data[9]. We’ve been humbled by an ecosystem of forward-thinkers – data providers, fintech developers, AI researchers – all putting their faith in us to change how this industry works[10]. That shared vision is our biggest asset.

- Enterprise Partnership: In a huge late-year milestone, SIX Group (Swiss, Spanish, and Aquis Exchanges) selected viaNexus to power its next-gen market data platform[11]. This dedicated cloud platform will span three exchanges, delivering data faster and more flexibly to customers at global scale[12]. The fact that an incumbent exchange group chose a startup like us speaks volumes. Our technology – high-velocity data delivery, fine-grained entitlement controls, resilient APIs – is purpose-built for exactly this mission[13]. And with vAST now in production, SIX’s data isn’t just streaming – it’s ready for direct integration into AI-driven workflows by their clients[14]. This partnership marked a real inflection point: a shift toward direct, intelligent data distribution as the new norm[15].

Lessons & Reflections: It wasn’t all smooth sailing (startups never are!). We learned to embrace the pivot – our initial “quick win” plan gave way to a far bolder vision once we saw the larger opportunity[16]. We also discovered that building a fintech platform means building a company and a community. From legal frameworks to customer feedback loops, we tackled the “not-so-glamorous” stuff that makes the difference between a cool demo and a sustainable business. On a personal note, I learned that naming and branding a company might be the hardest mission of all – who knew a logo could take so many iterations? (One surprisingly tough challenge was indeed branding; after a few false starts we landed on viaNexus, with a logo that captures our provider-to-consumer connection[17]). Through every challenge, one thing surprised me in the best way: the power of a small, focused team. Time and again, our team of ten-ish people punched above their weight, proving that a handful of innovators and skilled engineers can build infrastructure that reshapes how data flows in finance[18]. And we did it while having fun – weekly demos, the occasional Matrix joke, and that indescribable thrill of seeing our idea come to life.

What’s ahead? Without spoiling too much: 2026 is our year to scale[19]. We’re taking this rock-solid foundation and opening the floodgates – more datasets (across new asset classes and regions), deeper partnerships (some are already in the works), and an even tighter fusion of data with AI agents. In short, we aim to boldly go where no data platform has gone before[20] – enabling creativity, insight, and innovation in ways the legacy providers can’t. We’re just getting started, and I couldn’t be more excited.

Huge thanks to our customers, partners, investors, and the phenomenal viaNexus team (and my co-founders) who believed in this mission and poured their passion into 2025’s journey. You all made this possible[21]. The road ahead is bright – here’s to a bold, impactful 2026!

The recent article Defeating Nondeterminism in LLM Inference by Thinking Machines tackles one of the most overlooked challenges in AI: nondeterminism — the tendency for large language models (LLMs) to produce different answers even when given the same input.

It’s a dense, technical read, but the message is simple and important:

Even if you tell an AI model to give you the same answer every time, it might not — because the mathematics inside it isn’t perfectly repeatable.

Why identical inputs can yield different answers

Inside every LLM are billions of parallel calculations running on GPUs. These chips use “floating-point math” — lightning fast, but not perfectly precise. When billions of operations happen in parallel, even tiny rounding differences accumulate.

That means that (a + b) + c ≠ a + (b + c) once you scale it to model-sized maths.
The result: two identical prompts can produce slightly different answers, even at “temperature zero.”

In most settings, this is harmless — a different adjective, a slightly altered phrasing. But in finance, that same instability can turn a model from “innovative” into non-compliant.

When you’re generating valuations, growth rates, or risk ratios, “close enough” isn’t close enough.

The deeper problem: unreliable and unlicensed data

Even if you fixed the maths, most models fail at a deeper level — their data.

All foundational models are trained or extended on unverified web data. That includes scraped pages, public APIs, and community content — none of which are:

• Licensed for commercial or regulated use
• Guaranteed accurate, current, or complete
• Structured in a way that supports compliance or auditability

Worse, many LLM-based “finance agents” try to fill their knowledge gaps by reaching into the internet — scraping sites like Yahoo or Google Finance — to deliver “real-time” answers or prices

That’s not innovation; that’s a compliance nightmare waiting to happen. That data is not licensed for professional use, often delayed or inaccurate, and will not survive regulatory scrutiny. If your “AI agent” is scraping Google Finance for prices, you’re not automating insight — you’re setting yourself up for an audit and a big penalty.

viaNexus and vAST: fixing hallucination from the ground up

At viaNexus, we’re not trying to patch hallucination after it happens — we’re preventing it entirely by controlling the data fabric that AI operates on.

Our platform, viaNexus, and vAST (viaNexus Agentic Services Technology), are built to make hallucination impossible in structured domains.

Here’s how:

1. Deterministic, licensed data

Every dataset in viaNexus — filings, fundamentals, prices, symbology, news — is fully licensed, and permissioned to the agent level. Agents don’t guess what a number means; they query an exact, canonical source of truth. Our proprietary equity prices can also tell agents the how stocks and the market are performing right now (not 15 minutes or a day ago!).

2. Guardrails for agentic reasoning

vAST defines strict rules for how AI agents interact with data. Given the same inputs, you always get the same outputs. No improvisation, no drift, no mystery.

3. Precalculated market intelligence

LLMs are not good at maths. They were never built for precise calculation.
So at viaNexus, we precompute many of the statistics, ratios, and derived metrics that agents might otherwise try (and fail) to calculate in real time — things like intraday sector performance, valuation multiples, and change ratios. That means agents can focus on reasoning and explanation, while the platform handles the quantitative truth.

4. Separation of labor

We isolate structured data reasoning from free-text generation. LLMs can narrate, summarize, and explain — but they don’t calculate, and they don’t invent data because THEY DON'T NEED TO.

Why this matters

Current day AI systems in finance are brittle because they rely on nondeterministic computation layered over unlicensed, unstructured data.

If Thinking Machines is solving nondeterminism at the numerical level, viaNexus and vAST solve it at the semantic level — ensuring that financial data, logic, and meaning remain consistent, licensed, and reproducible.

Together, these two approaches define the foundation for trustworthy financial AI: numerical precision + semantic integrity + licensed data.

The future of agentic finance

As AI agents evolve to drive analytics, research, and decision-making, the industry faces a simple choice: Build on unverified data scraped from the web, or build on a deterministic, auditable, and licensed foundation.

At viaNexus, we’ve chosen the latter. Because in regulated, high-stakes domains like finance, hallucination isn’t something to mitigate — it’s something to eliminate.

Reach out to us if you'd like to work with us on defining the future of agentic finance. Meanwhile be sure to check out askNexus, our demo of how easy it is to build a compliant financial agent on vAST.

AskNexus AI Financial Assistant | viaNexus

AskNexus, AI Financial Assistant powered by Anthropic and viaNexus.

viaNexus

This article will delve into what constitutes an AI agent specifically how it applies to the financial sector, exploring its key dimensions and components and crucially differentiate it from the deceptive practice of "AI Agent Washing."

What is a Financial AI Agent?

An AI agent is an autonomous entity that operates with a given purpose. It independently seeks and utilizes relevant information to understand its environment, making decisions and adapting its behavior. Unlike traditional AI programs that adhere to predefined rules, AI agents learn continuously, rationalize outcomes, and determine subsequent actions to fulfill their purpose. They interact with their environment through effectors to achieve their goals.

Financial AI Agents take this a bit further, where they’re focused on augmenting the productivity of any financial analyst. Their role is to reduce the need for analysts' daily involvement in monitoring and collecting market movements, processing data, and generating reports allowing them to focus on more higher value revenue generating work, reducing their involvement on exhaustive rudimentary tasks.

Dimensions and Components of a Financial AI Agent

To fully grasp the concept of a Financial AI agent, it's helpful to understand the various dimensions and the core components that enable an AI Agents functionality.

Dimensions of an AI Agent

The effectiveness and sophistication of an AI agent can be evaluated across several key dimensions, we will enumerate them here:

• Purpose: The agents fundamental reasoning, the initial logic that will provide the agent with the motivation to and direction to accomplish its goal 
• Autonomy: This refers to the degree to which an agent can operate without direct human intervention. Highly autonomous agents can make complex decisions and execute tasks independently.
• Perception: Leveraging general knowledge(LLMs), agents will make sense of the information obtained to make decisions
• Reactivity: A reactive agent can perceive changes in its environment and respond in a timely manner. This dimension is crucial for agents operating in dynamic or real-time scenarios.
• Proactiveness: Proactive agents don't just react to their environment; they also initiate actions to achieve their goals. This involves planning, goal-setting, and anticipating future states.
• Learning: The ability to learn from experience, seek new information and then adapt to that information, improve performance over time is a hallmark of advanced AI agents.
• Adaptability: This relates to an agent's capacity to adjust its behavior in response to unexpected circumstances or changes in its operating environment.

Components of a Financial AI Agent

The internal architecture of a Financial AI agent typically comprises several core components that work in concert:

• Inputs: These are the input mechanisms through which the financial agent perceives its environment. Examples include financial data APIs, Financial News, Realtime and Delayed financial prices data, Financial data streams.
• Percept: The raw data received from inputs that are processed into meaningful directives, which form and provide the agent with a perceived state and environment
• Outputs: These mechanisms that allow the financial agent to act. This could involve a UI interface or communication module, a financial charting system, financial tool/function, or financial rules engine or alerting system.
• Contextual Memory: Ability to maintain memory from previous interactions in order to make guided decisions obtained from historical knowledge.
• Goal Representation: Financial agents have clear objectives or goals that guide their actions and decision-making processes.

AI Agent Washing: The Deception

As AI agents gain traction specially in the financial sector, a deceptive practice known as "AI Agent Washing" is emerging. This term refers to the misrepresentation or exaggeration of a system's AI agent capabilities to make it appear more advanced, autonomous, or intelligent than it truly is, where prompt engineering and prompt conditionals are miscategorized as learned outcomes, or decisions that are dynamically selected based on the current acquired information.  

vAST(viaNexus Agentic Service Technology)

At viaNexus, we are dedicated to empowering financial institution or individuals with the capabilities to construct truly autonomous Financial AI agents.

vAST (viaNexus Agentic Service Technology), provides a comprehensive suite of tools and resources designed to facilitate the rapid development of such agents by meticulously applying the critical dimensions of AI agents through an easy to implement opensource viaNexus agent SDK that will abastract away the integration of MCP and our extended OAuth2 protocol that allows for humanless agent authentication and authorization.

For instance, attempting to implement an AI Agent that has access to properly entitled and permissioned financial data is not just beneficial but imperative. When developing financial agents it is crucial that they fully embody the established dimensions of an AI agent. This means these agents must exhibit genuine adaptability to the dynamic and often volatile changes within the market. Beyond mere data processing, they should be capable of providing insightful and actionable suggestions on how to effectively navigate and respond to these shifts. This stands in stark contrast to many of today's so-called "AI Agent implementations," which, upon closer inspection, often amount to nothing more than sophisticated, yet fundamentally linear, workflows that can be adequately modeled and executed within a binary decision tree. True AI agents, as championed by viaNexus and enabled by vAST, transcend these limitations, offering a level of autonomy, responsiveness, and strategic thinking that redefines the potential of artificial intelligence.

At viaNexus we're creating and applying the fundamentals of Autonomous Agent design and utilizing vAST to build true autonomous agents that are actively monitoring our platform, the quality of our financial datasets, in addition to building external products and services like our very own Financial Assistant Agent "AskNexus" that provides realtime financial market insights through conversational interactions.

Go to https://vianexus.com and invoke a session with askNexus our Financial Assistant agent, which we ape(vibe) coded in 5min utilizing vAST, and experience how it learns, maintains contextual memory as it discovers its financial data environment to then be able to provide market analysis and insights.

The following video is a short demonstration of these capabilities.

Getting Started and Build your Financial Agent with vAST!

To begin developing your Financial Agent with fully entitled and permissioned financial data, create an account with viaNexus here. After signing up for either our free or paid tier, please contact us. As this product is currently in beta, we are eager to collaborate closely with you on the development of your first autonomous agents.

Previously, I wrote about how existing MCP implementation patterns impede scalable agentic environments, particularly concerning financial data entitlement and permissioned workflows.

Many of the current implementations treat MCP servers as mere clients or interfaces to datastores and API platforms, a pattern that is both a hack and fundamentally unscalable, especially when it comes to institutional grade authentication and authorization in the agentic world.

As agents become increasingly ubiquitous and integrated into our digital ecosystem, their capacity to operate autonomously will inevitably transition from a novel capability to a fundamental standard. To enable these agents to operate as distinct, identifiable, and accountable entities, their tokenization might be necessary, potentially leveraging Web3.x technologies… something for a future discussion.—for now agents must possess and represent a distinct legal or organizational entity in order to have some level of autonomy, and as they continue to evolve and integrate with advanced LLM capabilities, particularly in the areas of finance, the dawn of “ape 😂” (aka. vibe) coding in finance will be inevitable where front or back office workflows will and can be easily handled by digital employees or personas(agents) as well as autonomous agents that will be spawned and called on based on some relevant event that the agent has been predefined by an agent job description.

To facilitate this agile ideation-to-implementation pipeline, a critical prerequisite is the establishment of scalable and autonomous agents. These agents will necessitate seamless access to legal, reliable and high quality financial datasets with minimal, or ideally, no human intervention to configure, or authenticate. 

However, the journey towards fully autonomous financial agents is not without its challenges. Beyond data access, considerations such as ensuring regulatory compliance, managing ethical implications, and developing robust fail-safes for autonomous decision-making will be paramount, not to mention the business model for data monetization in general.

OAuth, currently being the most viable protocol, doesn’t completely satisfy the requirement of a truly scalable autonomous ecosystem. The enhancement of OAuth will require exploring decentralized identity solutions, granular access controls tailored for agent-to-data interactions, and real-time auditing capabilities to maintain transparency and accountability in an increasingly automated financial landscape. The ultimate goal is to create an ecosystem where ideation, powered by human ingenuity and refined by advanced AI, can be swiftly and securely operationalized through autonomous agentic workflows, driving innovation and efficiency across the financial sector.

Below, we explore viaNexus' comprehensive approach, which incorporates an enhanced OAuth protocol to apply these principles.

Our primary focus and illustrative example centers on the development of a sophisticated financial assistant agent, a critical application demanding absolute security and controlled access to entitled and permissioned financial data.

Identity and Representation

In the emerging agentic landscape, a vast new arena awaits. We foresee a future where "Digital Employees" are deployed to tackle intricate projects, swiftly coordinating agent teams, gathering pertinent data, and delivering comprehensive results to human stakeholders. But for legal (licensing), quality control, and performance this data cannot be just scraped off the web, or even sourced from market data feeds without the propor controls.

Until agents can self identify, carry wallets, and tokenize themselves as unique entities..hint hint, agents will need to be managed and will represent their human overlord who, rightly so, will want to know what their agents are up to, and what resources are they accessing, acquiring.

The viaNexus Agentic Workflow service aims to achieve fully autonomous operations by extending the OAuth 2.0/2.1 protocol to address the traditional OAuth 2.0 authorization flow's requiring synchronous user interaction for consent and token exchange. 

A key objective was to remove human intervention during the transport and handshake during authentication/authorization and at a very minimal instance an asynchronous communication regarding the agent's progress throughout its workflow.

Challenges with Traditional OAuth in Agentic Workflows

• User Consent Requirement: Standard OAuth relies on a user explicitly granting permissions through some human interaction like a web browser, which is impractical for institutional and scalable agents.
• Token Refresh and Expiry: Managing token refresh and expiry in a human-less environment requires robust automated mechanisms to prevent service interruption, data leakage.
• Security Implications: Bypassing human interaction introduces new security considerations, demanding enhanced safeguards against unauthorized access.

Our Solution: Agentic OAuth Extension

To overcome these hurdles, we've created a unique extension to the OAuth2 protocol. This extension adheres to RFC-6749 and RFC-6750 by utilizing existing protocol attributes. It heavily relies on a combination of pre-authorized scopes, secure service-to-service authentication, and a specialized token management system within our data platform. This system enables represented entities to asynchronously manage agents as they perform their duties.

Pre-Authorized Scopes and Service-to-Service Authentication

Instead of dynamic user consent, our system relies on pre-negotiated and tightly controlled scopes for each agentic workflow. This means that the permissions required by an agent are defined and authorized beforehand during the service's configuration.

The service-to-service authentication is facilitated through an established agent unique identifier and a secure key exchange mechanism, where the viaNexus Agentic Workflow service authenticates directly with resource servers without involving an end-user. This is achieved using:

• Agent Unique Id: viaNexus generates a unique identifier for each agent provides the ability to track and identify an agent
• Client Assertions: JWT-based client assertions are used to prove the identity of the viaNexus service client to the authorization server. These assertions are signed using a private key known only to the viaNexus service.
• Service Accounts: Dedicated service accounts with granular permissions are established on the resource servers, minimizing the attack surface.

Automated Token Management System

A critical component of our solution is the automated token management system. This system is responsible for:

• Proactive Token Refresh: The system monitors token expiry and initiates refresh requests with the moderator or represented entity through email and/or push notification, proactively, well before tokens expire, to ensure continuous operation.
• Secure Token Storage: Access and refresh tokens are stored in a highly secure, encrypted vault, accessible only by authorized internal components of the viaNexus service.
• Error Handling and Retry Mechanisms: Robust error handling and retry logic are implemented to gracefully manage network issues or authorization server unresponsiveness during token acquisition or refresh.
• Reporting and Agent monitoring: Agents are monitored, with reports detailing their scope access, permissioning, and overall activities.

The following diagram depicts the flow in practical terms:

viaNexus Agentic Workflow Service

Our OpenSource viaNexus Agent client SDK provides a seamless interface for integrating with the viaNexus Agentic Workflow service, abstracting away the underlying complexities of the extended OAuth protocol. Developers can leverage the SDK to trigger and monitor agentic workflows without needing to manage authentication tokens manually, permission scopes, asynchronous notifications and agentic payment services are integrated into the protocol.

We’ll demonstrate how the viaNexus AI services allow conversations with our financial data platform.

Autonomous Agents: Easily develop financial assistant agents

Leveraging Claude Code we develop an autonomous agent that will monitor portfolios for death crosses

The agent, named "Death Cross Financial Agent," will be configured with the following:

• Pre-authorized Scope: `financial_data.stats.read` - This scope grants the agent permission to access the necessary financial datasets.
• Event Updates: Using the platform Event Processor the Agent will receive events directly from the source
• Output: Alerts stakeholders with a comprehensive report by email.
• Runtime: Runs 24/7 analyzing data for death crosses

Single (human) User interaction with viaNexus data:

This section outlines a practical example of configuring the viaNexus MCP server in Claude Code to create Claude sub-agents which will intercept financial dialogue in Claude to assist with individual financial goals.

Conclusion

With our expanded OAuth protocol within the viaNexus Agentic Workflow service enables true human-less automation, unlocking new possibilities for efficient and autonomous business processes. The viaNexus Agent client SDK further simplifies integration, allowing developers to easily leverage these autonomous capabilities. For further details or to schedule a demonstration, please feel free to contact us a viaNexus

A year ago, practically to the day, three of us acquired a data platform from U.S. exchange IEX - with minimal diligence and a lot of conviction. That story was captured in Waters Technology, where it quickly became one of the most widely read articles of the year.

Since then, it’s been a journey. Today, I’m proud to say we are RRG — “ready for revenue recognition,” in Reuters-speak — and poised for what comes next.

From Acquisition to Rethinking the Model

Our initial plan was simple: lash everything together and win a few quick clients. But once we took stock, we realized the opportunity was much larger.

The platform we acquired had been built for scale and resilience, with a bunch of new capabilities that had been built after I left IEX in 2021 — perhaps most notably the ability to onboard data rapidly and automatically generate the highly normalized APIs which developers loved. We dubbed this “the Easy Button”.

At the same time, our research showed a clear market gap: independent data providers — many of them trusted colleagues and friends — needed better ways to deliver data to customers. On the other side, consumers were actively seeking alternatives to the expensive, inflexible bundles of the large aggregators.

We didn’t want to be a pure wholesaler, weighed down by the cost and subject-matter burden of building every dataset ourselves. Instead, the platform was perfectly positioned to connect creators of high-quality data with the firms that needed it — in an easy, on-demand, normalized form.

Why viaNexus Is Different

We’ve seen other attempts at data marketplaces — Snowflake, Amazon — but neither were built for financial services. They don’t monetize data effectively, normalize it for interoperability, or support low-latency streaming. Our platform was.

That realization gave rise to our mission:

To empower the future of finance by making data accessible, affordable, actionable, and insightful for everyone.

viaNexus is a financial data platform designed to democratize access to high-quality information. We do this through subscription-based access with flexible tiers. Clients can start small — say, data on 100 equities — and scale up to unlimited access. Like Amazon Prime, there’s a core bundle of 25 APIs - with broad appeal, plus premium datasets from expert partners and proprietary products from viaNexus.

For providers, we offer everything they need: transparent controls, fair revenue share, and freedom to set pricing. Our modern licensing agreement — tested in multiple deals — has proven simple, fair, and effective.

For consumers, we’ve made the Easy Button work too: no annual contracts, no hidden gotchas, just fast, fair, normalized APIs.

 Building the Content

Our growing partner network spans areas like news, earnings transcripts, fundamentals, earnings estimates, and indices.   

Alongside that, we’ve developed proprietary datasets such as:

• VNX — our U.S. equities real-time derived price feed, free from exchange licensing burdens, built algorithmically and with data sourced from multiple venues.
• Logos & Company Profiles — curated company metadata.
• Filings and as reported financials — normalized data from SEC Edgar, making filings LLM-ready.

Product, Design & Delivery

With the blueprint in hand, we moved fast. But we quickly realized: our engineering team were pipeline and feed experts, not web designers. We needed more than a “nice site” — we needed a seamless process for discovery, purchase, and consumption, fully integrated with Stripe – our payments provider

Enter Expero, a partner I’d worked with before. They helped us transform the vision into a design and workflow that works for both business and technical users.

At the same time, with support from Google, we tuned and upgraded the GCP-based platform for the scale we knew was coming.

Beyond the Tech: Building a Company

Platforms don’t succeed without companies around them. In year one we had to put foundations in place: legal entity, shareholder agreement, bank accounts, payroll, benefits, trademark registrations, budgeting, and financial controls. We were fortunate to have guidance and support from many along the way.

 One surprisingly tough challenge: branding. After research and false starts, we landed on viaNexus and a logo that captures our purpose:

• Two spheres — providers and consumers.
• An intersection — the Nexus.
• Square nodes — on both sides, our customers.
• A nod to the cloud — API-first and cloud-native.
• A bright, tech-forward palette.

Thanks again Expero for working with us on the logo, and creating the myriad of versions (image formats, full color, reverse color horizontal and vertical).

Team & Culture

They say “Culture eats strategy for breakfast.” It’s true.

From my fellow founders, to our talented engineers, to our partners at Expero, we are united by our mission. As a remote-first firm, we rely on stand-ups, weekly demos, and Slack to stay aligned — and connected as people. What helped most: our engineers knew the platform, had worked together at IEX, and were proud to see it go on to an even brighter future.

Our AI Story

Needless to say, AI is transforming financial services — and us along with it.  TBH we couldn’t have made the progress we’ve made without AI.

Internally, AI has boosted productivity: Cursor for coding, meeting companions in Google Meet, AI in Slack, Google NotebooksLM for servicing due diligence questionnaires (huge win!). Externally, we’re building for the next generation of agent-based workflows.

But we’re also realistic. Giving agents free rein to scrape uncontrolled data is a liability for customers: not just hallucinations, but rights and licensing. That’s why we’ve been vocal about agentic workflows that respect entitlements and IP.

We are closely watching Anthropic’s Model Context Protocol (MCP). It’s promising but still early. As our CTO Pedro Aguayo noted in July, there is work to do before it meets institutional standards - and rather than wait - he set to work enabling our data to seamlessly work with ALL LLMs, ALL agent frameworks - in a fully secure and entitled way. And by doing it right, there is no need for pages of prompt engineering – the intelligence is built in to the data.

Looking Ahead

The first year was about building the foundation. The next will be about unleashing it.

• Expanding datasets across asset classes and regions.
• Deepening partnerships with providers and institutions.
• Enabling AI-native, agentic consumption of financial data.
• Continuing to differentiate viaNexus as the speed-layer for finance.

We’ve rebuilt a platform loved by developers, expanded it with content, and refined it for today’s needs.

The future of financial data is open, agentic, and fast. viaNexus is ready.

If you have data, and would like to work with us – reach out.  If you are a builder, or just looking to upgrade your data at a lower price point – sign up and try us out.  The free tier is there for a reason.   

SEC filings are supposed to tell you everything you need to know about a public company — but they’re not designed for machines. Between inconsistent formats, ambiguous sections, and the complete lack of structure outside of XBRL tags, extracting meaningful information at scale is still a painful, manual process.

There are great tools for pulling structured financials from XBRL or iXBRL, and others that can extract addresses and tables — but what about the stuff that doesn’t fit into those schemas?

• Who’s the CEO, really?
• What does the company actually do, in plain English?
• Is there a business summary we can trust?
• Do we know how many people they employ?

These aren't just "nice to have" fields — they’re critical for decision-making, yet they typically require brittle scraping, manual review, or just accepting missing values.

At ViaNexus, that’s the problem we set out to solve.

The Gap: Alternative Data That’s Still Unstructured

There’s plenty of innovation around financial statement ingestion — and rightly so. But the “metadata layer” that surrounds a company’s core identity, leadership, and operations has mostly been ignored.

This information lives in raw HTML filings — not in XBRL. It’s inconsistent, fragmented, and hard to extract even for humans. But when cleaned up, it’s incredibly valuable for downstream systems: AI agents, dashboards, LLM summarizers, portfolio screeners, and more.

That’s why we built the SEC Company Descriptions pipeline: to transform overlooked metadata into structured, verifiable insight you can actually use.

The Pipeline: Designed for Today, Built for What’s Next

The pipeline runs monthly, processing thousands of US company filings to produce clean, consistent, and structured outputs. It uses resilient logic to handle messy HTML, applies intelligent filtering to ensure completeness, and integrates tightly scoped LLM prompts to summarize content without hallucination.

What makes it powerful isn’t just what it extracts — it’s how it’s built:

• Flexible parsing recovers from inconsistent formats
• Every field is logged and validated independently
• Gemini only sees scoped, cleaned content — reducing token usage by 95%+
• Alerts and coverage checks ensure issues never go unnoticed

It’s not agentic — yet — but it’s designed for that future. Structured inputs, minimal guesswork, and full traceability make this pipeline an ideal foundation for AI-driven workflows and automated intelligence.

Built-In Quality, So You Can Focus on What Matters

We built this pipeline so you don’t have to. It’s not a framework, a toolkit, or a how-to guide, it’s a fully managed data layer designed to slot directly into your systems.

Every layer — from extraction to QA — is engineered to deliver clean, reliable inputs straight into your models, dashboards, and agents. That means no brittle scrapers, no hallucinations, and no wasted cycles patching holes in your pipeline. Our job is to deliver structured insight you can trust. Quality isn’t an afterthought — it’s built into the architecture. This frees your team — and your stack — to focus on what actually matters: surfacing opportunities, generating alpha, and making decisions that move the needle.

We handle the plumbing. You capture the upside.

How Customers Are Using It

The SEC Company Descriptions dataset supports a wide range of use cases across finance, AI, and data infrastructure:

• Monitor executive changes and track business evolution using structured metadata
• Feed clean summaries into dashboards, research platforms, and LLM assistants
• Power alerting systems for leadership shifts or filing activity
• Reduce LLM token usage by isolating only the relevant content
• Enrich internal systems with standardized company context, linked to your existing data

Whether you're screening tickers, building chat-based tools, or maintaining knowledge graphs — this dataset helps you move faster with cleaner, more reliable inputs.

One of Many: Part of Our CORE Data Bundle

Company Descriptions is just one of over 20 datasets included in our CORE data bundle at ViaNexus — a foundational set of normalized, production-ready financial data that powers everything from dashboards to AI workflows.

While most datasets focus on prices, fundamentals, or corporate actions, Company Descriptions adds the missing layer: structured context around what a company actually does, who runs it, and how it describes itself. This makes it a natural complement to time-series and event-driven data — and a key enabler for smarter filters, alerts, and LLM prompts.

Other datasets in the bundle include:

• Historical prices
• Corporate actions
• Reference and symbology data
• Filing metadata…and more — all cleaned, linked, and continuously updated.

We use CORE’s own Symbols Reference Data to kickstart this pipeline — mapping active tickers to CIKs for SEC querying — and customers can access the same building blocks. For those looking to replicate this setup, our As Reported SEC Filings dataset offers full access to raw SEC filings enabling direct integration with unstructured 10K, 10Q, and 8K content. And if there’s a specific field or dataset you’re looking for, just reach out — we’re always looking to onboard more and support your workflow.

“You don’t need to teach an agent to read the whole filing — you just need to give it what matters.”

If you're working with SEC data, building AI infrastructure, or looking for trustworthy alternative data — we’d love to connect.

🔗 Learn more: https://vianexus.com/

📧 Contact us: support@vianexus.com

We will then introduce our Agentic Service implementation and outline how we are addressing these critical issues.

Current Shortcomings in Agentic Workflow Implementations

Seamless Authorization and Authentication

One of the most persistent hurdles in current agentic workflows is achieving truly seamless authorization and authentication. Agents often require human intervention to log into services, approve permissions, or navigate multi-factor authentication. While protocols like OAuth are powerful for user-centric applications, they are currently limited in their ability to handle agent-specific authorization and authentication without human intervention. This creates friction and limits the autonomy of agentic systems, requiring manual oversight for what should be automated processes. The challenge lies in designing a system where agents can securely and autonomously gain access to necessary resources without compromising security or requiring constant human approval, not to mention the inability for an agent to handle acquisition of resources.

Inability to Bypass Paywalls

Another significant limitation is the inability for agents to effectively get past paywalls. Many valuable online resources, from reliable  financial data, research papers to premium content, are locked behind subscription models or one-time payment gateways. Current agentic implementations struggle to autonomously process payments or credentials required to access this information. This restricts the scope of tasks agents can perform, as they are often cut off from essential data or services that reside behind a paywall, forcing human users to intervene and manually provide access.

Granular Permissions for Agent-Specific Workflows

The lack of granular permissions applied specifically to agent workflows presents a security and control challenge. In many systems, agents inherit the permissions of the human user who initiated them, or they are granted broad, undifferentiated access. This poses a risk, as an agent might inadvertently access or modify data beyond its intended scope, or identify an unauthorized agent attempting to access restricted data. 

There is a pressing need for a system that allows administrators to define and enforce highly specific permissions for each agent or agent workflow, ensuring that agents only have access to the minimum necessary resources required to complete their assigned tasks.

"Never Send A Human To Do A Machine's Job" -Agent Smith

Our Agentic Service Implementation: A Solution

At viaNexus, we recognize these limitations and are actively developing our Agentic Service implementation to provide robust solutions. Our approach focuses on creating an intelligent and secure framework that empowers agents with greater autonomy and control.

Here's how we are addressing these shortcomings:

• Enhanced Authorization and Authentication Mechanisms: We are developing advanced authorization and authentication protocols tailored for agentic systems. This includes leveraging new cryptographic techniques and secure credential management systems that enable agents to authenticate themselves without constant human oversight, while still maintaining high levels of security. Our aim is to move beyond the limitations of traditional OAuth for agent-to-service interactions, exploring methods that facilitate seamless, machine-to-machine authentication.
  • Extended OAuth: Eliminate human intervention during the Authentication and Authorization handshake, this all occurs during the transport of the protocol.
  • Enhanced Timed Bearer Tokens: Agents are unique to an Organization identified by UUID, will require represented entity approval via asynchronous push notifications for bearer token creation and refresh.

• Fine-Grained Permission Control: We are implementing a comprehensive, granular permission system for agent-specific workflows. This system will allow administrators to define precise access policies for each agent, specifying exactly what resources an agent can access, what actions it can perform, and under what conditions. This ensures that agents operate within clearly defined boundaries, enhancing security and preventing unauthorized access.

• Intelligent Paywall Navigation: Our Agentic Service incorporates mechanisms to intelligently navigate and interact with paywalls. This involves integrating secure payment processing capabilities that allow agents to use pre-authorized payment methods or credentials to access paid content. This feature will enable agents to access a wider range of information and services, significantly expanding their utility.
  • Agents will have secure access to the credit cards of represented entities. This feature will be enabled through our OAuth extension and detailed permissioning schemes and scopes.

Our Agentic Service is designed to be a significant step forward in making agentic workflows truly autonomous, secure, and powerful. We believe that by tackling these fundamental challenges, we can unlock the full potential of agent technology, paving the way for more efficient and intelligent automated systems.

The following clip displays the ease and power of the viaNexus Agentic Workflow, fully incorporating agent Authorization and Authentication using the opensource viaNexus MCP client sdk, by quickly building an agent leveraging Replit, an online AI software development tool that allows the user to dialogue with the developer agent while it writes the software.

It's important to note:
This architectural decision yields significant advantages. Foremost, it dramatically reduces the system's dependency on external data sources, thereby mitigating the inherent risks associated with third-party data integration, such as inconsistencies, security vulnerabilities, or availability issues. Furthermore, by strictly confining data retrieval to the viaNexus Data Platform, the risk of "hallucinations"—where an AI generates plausible but incorrect information—is virtually eliminated. Consequently, the ultimate reliability and integrity of the data presented to the user are directly and solely contingent upon the stringent data quality standards upheld by viaNexus. This controlled environment ensures a high degree of accuracy and trustworthiness in all financial data interactions.

For an exclusive early look at viaNexus's Agentic Workflow service, join our limited beta program. Experience firsthand how this revolutionary service can boost your operational efficiency and intelligent automation. Interested? Contact us at mcp-beta@vianexus.com to discuss how you can participate and be among the first to leverage next-gen intelligent workflow automation.

For decades, the sourcing, licensing, and onboarding of financial data has followed a well-worn path: procurement teams negotiating lengthy contracts, legal teams enforcing entitlements, engineering teams wiring up APIs to serve well-defined application needs - and data providers auditing their customers to ensure adherence to the license! These deals are complex by design, intended to maximize value and tightly control usage. The result: a highly structured ecosystem, where data flows only where it’s been explicitly permitted to go.

But AI doesn’t follow the old playbook.

Large Language Models (LLMs) and autonomous agents are changing how people—and increasingly, machines—interact with information. These technologies are no longer theoretical. They’re generating research reports, optimizing logistics, managing customer support, and even autonomously trading. And they’re hungry for market data.

The Rise of Autonomous Agents—and the Licensing Blind Spot

Today's AI agents don’t wait for a formal API contract to be signed. With tools like Replit or Lovable (yes, it’s a dev platform, not a dating app), anyone—regardless of coding background—can describe an application and have it built automatically. These systems crawl documentation, stitch together calls to public APIs, and do their best to deliver functional code.

In one recent test we conducted, we had Replit build a simple market data app for the Dow 30, assembling company descriptions, CEO names, LEIs, and more. The code behind it had helpfully pulled data from Yahoo Finance—a source not licensed for professional use. The model didn’t ask if the data was entitled or even legal to use. It just vibed its way to an output.

This “vibe coding” approach hasn’t yet landed inside regulated financial institutions, but it’s only a matter of time. And it exposes a growing gap between how data is supposed to be consumed and how it will be consumed in the era of agents and AI.

APIs Aren’t Agent-Ready

Traditional APIs are built for deterministic applications. They’re like hard-wired power cords—great for static, repeatable workflows but poorly suited to the dynamic, exploratory nature of agentic interactions.

Agents don’t ask for a specific endpoint or dataset. They ask questions:

• “What’s going on with NVDA today?”
• “What’s the risk exposure in my portfolio?”
• “Summarize the latest SEC filings for my holdings.”

They need to reason across data types, blend sources on the fly, and adapt to changing contexts. Yet most market data APIs differ in structure, symbology, and entitlements. Even if an agent is authorized, it may not be able to authenticate. And once inside, parsing schema mismatches or reconciling date formats becomes a major hurdle.

Today’s LLMs often compensate by scraping the web or connecting to open sources—but these are frequently incomplete, inaccurate, or misinterpreted. Hallucinations are common, and hallucinated data are a regulatory nightmare waiting to happen.

A New Standard: MCP and the Promise of Agentic Data Access

To address this, a new set of protocols is emerging. Chief among them is MCP (Model Context Protocol), an open standard pioneered by Anthropic that allows agents to discover, access, and use data with embedded context and permissioning.

Think of MCP as “USB-C for Data.” It’s machine-native, schema-aware, and designed to work across disparate sources. Google’s Agentic Development Kit (ADK) follows a similar philosophy, focusing on seamless access to internal resources like CRMs and emails.

These protocols open the door to a world where agents can safely and intelligently connect to proprietary data stores—provided the data is accessible, entitled, and machine-readable. In a recent keynote on Claude Opus 4, Anthropic showcased agents writing code on the fly, searching the web for real-time financial data to perform “complex financial analysis.” But anyone familiar with market data knows that’s a dangerous assumption. Real-time financial data is neither freely available nor license-free!

Still Nascent—But Moving Fast

The technology is promising, but early. As of today, only Claude has released a working MCP client paired to an LLM, meaning most demos (and agent use cases) are locked into Anthropic’s ecosystem. And crucial elements like entitlement enforcement and secure authentication are not yet solved. Most current MCP examples rely on open or internal data, avoiding the messy complexity of licensed content.

Still, the potential is real. These protocols offer:

• Lower integration costs
• Reduced switching friction
• Faster development of user-generated applications
• A move toward peer-to-peer data ecosystems, rather than aggregator-dominated ones

They also hint at a new commercial model for market data—one that is more consumption-driven, dynamic, and “AI-agent ready.”

But Not Without Risk

As this technology matures, there will clearly be growing pains. We should expect:

• Unapproved Data leakage becomes easier, not harder.
• Entitlement breaches become more likely, with traditional audit processes struggling to keep up.
• Licensing frameworks that may crack under the weight of autonomous agents.
• Vendors may look to insert proprietary “back doors” into the agent ecosystem, creating lock-in rather than openness. Regardless they will charge more!

But the genie isn’t going back in the bottle. The challenge for our industry is to create a framework where agents can operate responsibly—with appropriate entitlements, auditable access to trusted sources.

What We’re Doing at viaNexus

At viaNexus, we believe MCP will be a critical bridge between trusted financial data and the AI agents that increasingly rely on it. We’ve been quietly integrating MCP capabilities into our platform—and the results are extraordinary. Our goal is to create a high-performance, permissioned data layer that enables AI-native discovery and access, while respecting the rights and business models of data creators.

Expect more on this soon.

The era of API-first market data delivery is evolving—into a future where agents need data that is permissioned, contextual, and AI-native. Let’s build the standards to make that future sustainable, secure, and open.

Remember that iconic scene in The Matrix – when Trinity is instantly equipped with the knowledge to pilot a Bell 212. Well - it isn’t just science fiction anymore - kinda! In the world we’re building at viaNexus, it’s becoming a metaphor for how intelligent agents will interact with data.

When an agent hits a use case that requires highly specialized data – say, granular pre-trade analytics from BMLL- soon it will be able to request access via our Model Context Protocol (MCP) server.   

Very soon. What does that mean? Agents and their human masters will have seamless access to this highly specialized dataset, without the need for heavy weight integration, minimal if any prompt engineering. 

Just have a conversation with the data! Hallucination free!We’re currently testing this new piece of kit and have been truly amazed how it works seamlessly with the viaNexus platform. Now common or garden LLMs (like Claude, or ChatGPT) can be transformed into an expert agents in data driven domains – just like Trinity become a helicopter pilot - now watch the video and you'll get the idea!

Sitting on top of viaNexus, this MCP layer acts as the smart bridge between AI applications and the right data set, at the right moment, in the right format. Only possible because of our well documented, normalized and superfast APIs.

No more pre-loading the entire world of data.  No more hunting for access or decoding obscure schemas.

Human: “Show me how IBM trades, by venue over the last 3 months.”

LLM: “I have access to BMLL venue data, let me access that for you.”“

"Would you like that as a chart or download into Excel?”

BMLL Technologies is the leading, independent provider of harmonized, Level 3, 2 and 1 historical market data and analytics to the world’s most sophisticated capital market participants. 3 of their most demanded derived datasets are available on viaNexus – for both professional and personal use:

1: Daily Classified trades: A normalized view of all trades across every venue, aggregated daily by venue and trading mechanism.

2. Auction Analytics: detailed measurements on auction dislocations and imbalances, supporting the evaluation of liquidity dynamics, slippage and price dislocations, execution strategies, and venue performance.

3. Retail Trades: Beyond top-line volume and uncover retail share of market volume, venue fragmentation patterns and shifts in behavior by instrument.The future of data access is contextual, dynamic, and intelligent.