Blog · 2026-07-29 · Ashutosh Agarwal

Matterfact vs. Claude: what each one does, and why funds run both

Claude is a brilliant assistant. Matterfact is the research platform built around it: connected to your fund's own data, shared across your team, and able to work with gigabytes of market and alternative data. A plain-English comparison, with a table you can scan in thirty seconds.

TL;DR

• Claude is a frontier AI assistant. Matterfact is a research platform for investment teams, built on top of frontier models, including Claude itself
• The difference is not intelligence. It is everything around the intelligence: your fund's internal data, your team's shared skills and memory, and applications that can hold real market data
• One table below shows, at a glance, what Matterfact has that Claude alone does not
• If your team already pays for Claude, Matterfact does not replace it. It makes it dramatically more useful

We hear a version of the same question in almost every first conversation with a fund:

"Our analysts already use Claude. Why would we need Matterfact?"

It is the right question, and it deserves a straight answer rather than a sales line. So here is the honest version, written for analysts rather than engineers.

The short answer: Claude gives each of your analysts a brilliant assistant in a chat window. Matterfact turns that intelligence into your fund's research platform. Connected to your internal systems. Shared across your team. Able to build applications that hold gigabytes of market data. And running on whichever frontier model happens to be best at each task, which today includes Claude itself.

That is not a knock on Claude. We think Claude is the best reasoning model in the world for investment research, which is exactly why Matterfact runs on it. The comparison is not engine versus engine. It is the engine versus the vehicle built around the engine.

heading: What we cover
What Claude is, in plain terms
Where the gap shows up
The quick answer, in one table
Skills your whole team shares
Memory that belongs to the fund
Your internal data, connected
Market data and alt data at real size
Applications, not just chats
The web, watched for you
The best model for each task
What this looks like on a Tuesday
So do you still need Claude?

What Claude is, in plain terms

Claude is a frontier AI model made by Anthropic, wrapped in a set of well-designed products: a chat app, projects where you can store files and instructions, and Artifacts, which are small interactive pages Claude can build for you inside a conversation.

For an individual analyst, it is genuinely excellent. It reads long documents carefully, reasons through ambiguity, writes clean prose, and does not need a manual. If you want to summarize a transcript, pressure-test a thesis, or draft the first version of a memo, Claude is as good as it gets.

But Claude the product is built for everyone: lawyers, marketers, students, software engineers, and you. On day one it knows nothing about your fund. It does not know your coverage list, your live research, what your PM asked about last week, or what your firm considers credible evidence. And its features stop at the edge of the chat window. What one analyst builds in their account stays in their account.

None of that is a flaw. It is the natural shape of a general-purpose assistant. The gap only becomes visible when a whole investment team tries to run its research process on one.

Where the gap shows up

Put ten analysts on Claude and the same five problems appear within a quarter. None of them are about how smart the model is.

label: The five gaps a fund runs into
It starts from zero :: Every conversation begins with no knowledge of your fund, your coverage, or your prior work. Analysts spend the first ten minutes of every chat re-explaining context.
Work stays personal :: Prompts, projects, and memory live in individual accounts. When your best analyst leaves, their way of working leaves with them.
Files, not data :: Uploads are measured in megabytes. A single day of market data, or one alternative data feed, is measured in gigabytes. It simply does not fit.
Integrations are your problem :: Claude supports custom connectors, but your fund has to build, secure, and maintain each one. That is an engineering project, not a setting.
One model family :: Claude is Claude. When another model is better at web research or coding, there is no way to route the task to it.

Matterfact exists to close those five gaps. Here is the whole comparison in one place.

The quick answer, in one table

This is the thirty-second version: what Matterfact has that Claude, on its own, does not. Everything in the right-hand column is something your team would otherwise have to build, staff, and maintain itself.

Claude capabilities are referenced from Anthropic's public documentation as of July 2026. Both products improve quickly, and we update this comparison when they do.

The rest of this post walks through each row in plain English, because the rows compress a lot.

Skills your whole team shares

A skill, in Matterfact, is a written-down, tested way of doing a piece of analyst work. Think of it as the difference between asking a smart intern to "look at the quarter" and handing them your firm's actual earnings-review checklist: which line items to bridge, which guidance language to compare against last quarter, which sell-side numbers to pull, and what the finished output should look like.

Matterfact ships with more than 5,000 of these skills, covering idea generation, diligence, earnings review, modeling, and thesis tracking across 163 GICS sub-industries. A software analyst's earnings review is not the same as a bank analyst's, and the skills reflect that.

The part that matters for a team: skills are shared. When a senior analyst refines the way your firm reviews a print, that refinement becomes how everyone's assistant reviews a print, the same afternoon. In Claude, the equivalent knowledge lives in personal projects and prompt files, maintained separately by each person, drifting apart quietly.

If you want to see what this looks like in practice, the platform overview walks through the skill library in detail.

Memory that belongs to the fund

Ask an analyst what they would want an assistant to remember and the list writes itself: the sell-side research the firm receives, internal notes, IC memos, expert call transcripts, models, and the diligence folder from the last three years.

Claude has memory, and it is genuinely useful, but it is personal memory. It accumulates for one user, inside one account.

Matterfact maintains a persistent research layer for the whole fund. Every conversation, skill, and application can draw on it. Three practical consequences:

• A new analyst inherits the fund's accumulated context on day one, instead of rebuilding it over eighteen months.
• The answer to "what did we conclude about this name last year, and why?" is retrievable, with the underlying documents attached.
• Two analysts covering adjacent sectors stop unknowingly duplicating work.

Your internal data, connected

This is usually the row that decides the evaluation.

Claude supports connecting external tools through custom connectors (the underlying standard is called MCP). The capability is real, and we use the same standard ourselves. But notice what "supports" means in practice: your fund identifies each internal system, builds a connector for it, secures it, and maintains it as the system changes. For a fund without a large engineering team, that is where the project stalls.

Matterfact's AutoMCP does that mapping automatically. It connects to the systems your fund already runs, including your portfolio database, your order and execution systems (OMS/EMS), risk reports, the blotter, and administrator feeds, and makes them available to every skill and application, with permissions.

The result reads simple in a sentence and changes daily work completely: an analyst can ask a question that touches live internal data, in plain English, and get an answer grounded in your fund's actual numbers rather than the public internet.

Market data and alt data at real size

Here is a size comparison worth sitting with for a moment. A PDF of an annual report is a few megabytes. One day of consolidated US equity market data is measured in gigabytes. A year of a web-scraped pricing dataset, likewise. Chat products are built for the first kind of file. Research is done on the second.

label: File-sized vs. feed-sized
tone: verdict
left: What fits in a chat upload
right: What research actually runs on
An annual report or a deck :: Tick and daily market data across your coverage
A spreadsheet extract :: A vendor alt-data feed, delivered continuously
A folder of transcripts :: 120M+ podcast episodes, indexed and searchable
A CSV someone trimmed to fit :: The untrimmed dataset, with full history

Matterfact treats feeds as first-class citizens. Market data, alternative datasets, web-scraped sources, and our own podcast intelligence corpus are ingested, kept current, and made available to skills and applications at their full size. Nobody trims a CSV to fit under an upload limit, and nobody's analysis quietly depends on the trimming.

Applications, not just chats

Claude's Artifacts deserve real credit: they made "ask for a tool and watch it appear" a normal thing to do. But an Artifact is a self-contained page. It has no backend, no database behind it, and no concept of your team. It is a demo of the tool you want, rather than the tool.

Matterfact builds persistent applications: an earnings-season tracker for your coverage, a live sum-of-the-parts model, a screener that runs over your internal data and your alt-data feeds at the same time. These applications have a backend, shared state, permissions, and multiple views. Build once, and the whole team opens the same app on Monday morning, showing current data, with everyone looking at the same numbers.

label: A page vs. an application
tone: verdict
left: A Claude Artifact
right: A Matterfact application
Lives inside one person's conversation :: Deployed once, opened by the whole team
Holds the data pasted into it :: Queries live feeds and internal systems
Refreshes what it was given :: Stays current as new data arrives
No permissions or roles :: Permissioned like the rest of your stack
Rebuilt when someone wants changes :: Versioned, improved, and kept

The web, watched for you

A surprising amount of research maintenance is just watching web pages: a competitor's pricing page, a regulator's docket, a company's hiring page, a distributor's inventory listings. Claude can research the web when you ask, and it can run scheduled prompts. But turning that into a reliable pipeline, where pages are checked on a schedule, changes are extracted into structured data, and the right person gets told, is a system your team would have to build.

Matterfact's agentic web scraping is that system. You point it at the sources that matter for your coverage. It monitors them on a schedule, structures what it finds into datasets, and routes changes into alerts and into the research layer, so a change shows up both in your inbox and in the data your skills and applications query. There is a deeper dive on the web scraper page.

The best model for each task

Claude is the model we reach for on reasoning-heavy research. But it is not the best at everything, because no single model is. Today, Gemini leads on web-scale search tasks and Codex on certain coding work. Next year the leaderboard will look different, because it always does.

Matterfact is built as an orchestration layer across frontier models: Claude for reasoning, Gemini for web search, Codex for coding, with the flexibility to re-route as model quality shifts. Your analysts never think about it. They ask their question, and the platform quietly uses the right engine for it.

For a fund, this is an insurance policy as much as a feature. Committing your research process to a single model family means re-platforming when the landscape moves. Committing to an orchestration layer means the landscape moving is someone else's problem. Ours.

What this looks like on a Tuesday

Strung together, the pieces above turn into a fairly ordinary-looking morning:

label: Earnings day, with the platform doing its share
numbered: true
The print drops :: A company on your coverage reports before the open
The skill runs :: Your firm's earnings-review skill bridges the actuals against consensus and your internal model
The app updates :: The team's earnings tracker reflects the print, the guidance change, and the transcript, live
The team is told :: Analysts on the name get an alert with the five things that changed
You go deeper :: You ask follow-up questions in plain English, grounded in your fund's data and memory

Every step above is possible with a general-purpose assistant plus a lot of engineering. The difference is that here, nobody at your fund had to build any of it, and nobody has to maintain it.

So do you still need Claude?

Yes, and this is worth saying clearly: Matterfact does not replace Claude. It completes the platform around it.

If your analysts have Claude subscriptions today, nothing about that is wasted. Claude remains a superb personal assistant for the hundred small tasks that never touch fund data. And because Matterfact runs Claude under the hood, choosing Matterfact is not choosing against Anthropic. It is choosing to point Anthropic's best model at your fund's actual data and workflows.

We even ship a Matterfact connector for Claude, so you can pull our podcast intelligence into a Claude chat directly. We are, unapologetically, fans.

The one-line summary, if you need it for a memo: Claude gives every analyst an assistant. Matterfact gives the fund a platform.

FAQ

Does Matterfact replace Claude?

No. Matterfact is built on top of frontier models, including Claude. It adds the layer a fund needs around the model: internal data integration, shared skills and memory, applications that hold real data, and orchestration across models.

Which models does Matterfact run on?

Claude for reasoning-heavy research, Gemini for web-scale search, Codex for coding tasks, and other leading models as the landscape evolves. Routing happens automatically, and we re-evaluate it as model quality changes.

Where does our data live?

In a single-tenant deployment: your fund's own isolated environment, running on AWS and GCP. Your data is never pooled with other funds and is never used to train models. The details are on our security page.

We already built some Claude connectors ourselves. Is that wasted work?

No. Matterfact speaks the same standard (MCP), so existing connectors can plug in. AutoMCP covers the systems you have not wired up yet, which for most funds is most of them.

Can we start small?

Yes. Many funds start with a single team or a single workflow and expand from there once the value is obvious.