Blog · 2026-07-29 · Ashutosh Agarwal

Matterfact vs. Claude: why investment teams need purpose built platform

Claude is a brilliant assistant. Matterfact lets investment teams turn a specific thesis into a shared application: one that combines internal data, alternative data, and the web, then keeps the evidence current.

TL;DR

• Matterfact is a research platform for investment teams, built on frontier models, including Claude itself.
• Great investors do not apply one model to every company. Matterfact lets them build a living application around each investment thesis instead.
• Those applications combine internal data, purchased alternative data, and web-scraped evidence in one shared place—and stay current as the thesis changes.

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.

The short answer: Claude gives each analyst a brilliant assistant in a chat window. Matterfact lets the fund build a living application around each investment thesis. The application brings together the firm's internal data and research portals, the alternative data it has bought, and evidence gathered from the web. It is shared across the team, can hold real market data, and runs on the best frontier model for the task—today, often Claude itself.

That is not a knock on Claude. We think Claude is the Rolls-Royce engine for investment research, which is exactly why Matterfact runs on it. But a fund cannot run its process on an engine alone. It needs the aircraft around it: the systems, data, and controls that turn a powerful engine into something the whole team can fly.

heading: What we cover
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
Why applications beat generic SaaS
The web, watched for you
The best model for each task
So do you still need Claude?

Where the gap shows up

Claude is built for everyone: lawyers, marketers, students, software engineers, and investors. On day one, it knows nothing about your fund: your coverage list, live research, what your PM asked last week, or what your firm considers credible evidence. It gives every analyst the same blank canvas.

That is not a flaw; it is the natural shape of a general-purpose assistant. The same is true of generic research SaaS. A generic dashboard may fit every company a little, but it will not fit the question that actually determines an investment: is this company gaining share because of product velocity, pricing, distribution, or something else?

The best investors do not use one model for every company. They form a distinct view of each one, identify the handful of facts that could prove it right or wrong, and follow those facts over time. Their software should work the same way. Put ten analysts on a general-purpose assistant—or the same off-the-shelf dashboard—and the same five problems appear within a quarter. None are about how smart the model is.

label: The five gaps a fund runs into
No internal research layer :: Your firm receives hundreds of sell-side reports, expert-call notes, and internal research every day. Claude does not build and maintain the retrieval layer that makes that accumulated work available when an analyst needs it.
Work stays personal :: Prompts, projects, and memory live in individual accounts. When your best analyst leaves, their way of working leaves with them.
Prototypes, not live applications :: Claude can help you prototype a tool in a chat. It does not give your team a live application they can return to every day: connected to current data, shared with colleagues, and built to evolve with the thesis.
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 gaps by making applications—not chats or generic dashboards—the unit of research. Here is the 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.

Why applications beat generic SaaS

An investor looking at a consumer company may need to track repeat purchase, pricing, and new-store productivity. An investor looking at a bank may need deposit flows, credit losses, and capital ratios. Neither should have to squeeze those questions into the same generic SaaS screen.

That is why Matterfact builds persistent, thesis-specific applications. An application might be an earnings-season tracker for a coverage list, a live sum-of-the-parts model, or a dashboard that watches the few indicators that would change the team's view on a company. It brings internal data, purchased alt-data, and web-scraped sources into one place—not because more data is better, but because the thesis is easier to test when the relevant evidence sits together.

Unlike a generic SaaS dashboard or a self-contained chat artifact, these applications have a backend, shared state, permissions, and multiple views. Build once, and the whole team opens the same current application on Monday morning, looking at the same evidence and the same numbers.

label: A page vs. an application
tone: verdict
left: A generic page or dashboard
right: A Matterfact thesis application
Starts from a generic workflow :: Starts from the questions that determine a specific thesis
Holds the data pasted into it :: Combines live feeds, web evidence, and internal systems
Refreshes what it was given :: Stays current as new evidence arrives
No shared working context :: Permissioned and shared with the investment team
Rebuilt when someone wants changes :: Versioned as the team's thesis evolves

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 to a thesis. It monitors them on a schedule, structures what it finds into datasets, and routes changes into alerts and the research layer. The evidence shows up both in your inbox and in the application the team uses to test that view. 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.

Yes. Claude and ChatGPT are core applications for any modern firm. Your team should use them for the broad, everyday work they are excellent at: writing, brainstorming, coding, learning, and the hundred questions that do not need a fund's research infrastructure.

Investment research is different. It depends on the fund's accumulated knowledge, live internal systems, alternative data, and the specific facts that could confirm or break a thesis. That is where Matterfact belongs: as the platform that turns those inputs into shared, living applications for the investment team.

Choosing Matterfact is not choosing against Claude or OpenAI. Matterfact runs frontier models—including Claude—and orchestrates them around the data and workflows that make investment research distinct. We even ship a Matterfact connector for Claude, so you can pull our podcast intelligence into a Claude chat directly.

The one-line summary: Use Claude and ChatGPT for general work. Use Matterfact to run investment research.

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: shared skills and memory, internal data integration, thesis-specific 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.