Finbar is launching public availability of its data and research platform to all investors (and their agents) - already used by several of the world's largest hedge funds. Services include: worldwide fundamental data via API/MCP; prebuilt company models and primers; and a complete AI suite for research and modelling (on web and in Excel).
Hi! Ed and Robin here, super excited to launch the first general availability of Finbar. Our mission is to give every investor (and agent) the best data and tools to conduct high-quality financial research.
A brief recap of our journey. We started with a single product - reliably updating any financial model within 5 minutes of market release, without any mistakes. Seems niche? It was - but it meant we could truly stand out to customers, solving a real burning problem that those who haven't worked in hedge funds just don't really 'get'. Over Q2 earnings season, we handled 150,000 model updates, meaning our clients (including several of the world's largest hedge funds) could go into every market-open with all of their models ready.
Developing this product turned out to be a problem that seemed simple but actually was fiendishly difficult (not least for the data reasons I discuss below). Solving it, however, has meant building some truly unique financial data and infrastructure, which we've since deployed in many other products - and now available to all investors.
Two key parts to what we do:
I. We're building the best fundamental financial dataset
Underlying everything is our proprietary data. We collect and ingest millions of documents across thousands of companies worldwide: reports, presentations, transcripts. You might be surprised this is not a solved problem already. But almost all providers lack at least one of: a) breadth, b) quality, and/or c) speed. All three have been necessary to serve our clients, hence the need to build our own. And yes, we're including players like Factset, S&P and Bloomberg in this (we're often explicitly evaluated against them by our institutional clients).
Some snippets to illustrate the point:
The most important fundamental datapoints are often not in 10Qs/10Ks, and rarely in a fully-structured format. Think segment organic growth in presentation charts, conference call remarks on forward guidance, Pillar 3 banking disclosures, and a million other company-specific datapoints.
>1/2 of our customers invest outside of the US. International markets have much less structured data availability than the US, often not even a central repository, and it is where arguably there is more edge from strong fundamental data (these two facts may well be corollary)
Financial data is exceptionally messy on the tail end - which is often also where the insights live. Understanding nuances around restatements, accounting regime changes, mergers and divestments, and all the other insane things companies do to produce messy data (sometimes on purpose) is bread-and-butter in our design. It causes many nights of hair-pulling but is the only way to actually produce great data that investors trust.
II. We're building the best financial toolkit for the AI age
Upon this data foundation is a whole range of Finbar products, built on the knowledge and experience of what professional investors actually need. This includes:
Machine-readable versions of millions of worldwide company documents
Fundamental time-series and datapoints on thousands of line items
Company primers and financial metadata
Prebuilt public company model XLSXs
A web platform for AI-automated financial data analysis, modelling, and report-writing
An Excel add-in to update any analyst model in seconds, and build any custom model in minutes
MCP and APIs to expose our fundamental data to programmatic and agentic users
And we already have a long list of items in development with institutional partners, including macro data, supply chains, newsflow, privates and more.
There’s still so much more we’ve got planned, but we’re genuinely so excited to start giving every investor the tools they need to make intelligent decisions in the AI age. So thanks for reading, and head to https://finbar.com to try us out. We’re looking forward to your thoughts!
About Finbar on Product Hunt
“Agentic investment research”
Finbar launched on Product Hunt on October 2nd, 2026 and earned 105 upvotes and 5 comments, placing #18 on the daily leaderboard. Finbar is launching public availability of its data and research platform to all investors (and their agents) - already used by several of the world's largest hedge funds. Services include: worldwide fundamental data via API/MCP; prebuilt company models and primers; and a complete AI suite for research and modelling (on web and in Excel).
On the analytics side, Finbar competes within Fintech, Investing and Data & Analytics — topics that collectively have 80.4k followers on Product Hunt. The dashboard above tracks how Finbar performed against the three products that launched closest to it on the same day.
Who hunted Finbar?
Finbar was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Finbar including community comment highlights and product details, visit the product overview.
Garry Tan
Hi! Ed and Robin here, super excited to launch the first general availability of Finbar. Our mission is to give every investor (and agent) the best data and tools to conduct high-quality financial research.
A brief recap of our journey. We started with a single product - reliably updating any financial model within 5 minutes of market release, without any mistakes. Seems niche? It was - but it meant we could truly stand out to customers, solving a real burning problem that those who haven't worked in hedge funds just don't really 'get'. Over Q2 earnings season, we handled 150,000 model updates, meaning our clients (including several of the world's largest hedge funds) could go into every market-open with all of their models ready.
Developing this product turned out to be a problem that seemed simple but actually was fiendishly difficult (not least for the data reasons I discuss below). Solving it, however, has meant building some truly unique financial data and infrastructure, which we've since deployed in many other products - and now available to all investors.
Two key parts to what we do:
I. We're building the best fundamental financial dataset
Underlying everything is our proprietary data. We collect and ingest millions of documents across thousands of companies worldwide: reports, presentations, transcripts. You might be surprised this is not a solved problem already. But almost all providers lack at least one of: a) breadth, b) quality, and/or c) speed. All three have been necessary to serve our clients, hence the need to build our own. And yes, we're including players like Factset, S&P and Bloomberg in this (we're often explicitly evaluated against them by our institutional clients).
Some snippets to illustrate the point:
The most important fundamental datapoints are often not in 10Qs/10Ks, and rarely in a fully-structured format. Think segment organic growth in presentation charts, conference call remarks on forward guidance, Pillar 3 banking disclosures, and a million other company-specific datapoints.
>1/2 of our customers invest outside of the US. International markets have much less structured data availability than the US, often not even a central repository, and it is where arguably there is more edge from strong fundamental data (these two facts may well be corollary)
Financial data is exceptionally messy on the tail end - which is often also where the insights live. Understanding nuances around restatements, accounting regime changes, mergers and divestments, and all the other insane things companies do to produce messy data (sometimes on purpose) is bread-and-butter in our design. It causes many nights of hair-pulling but is the only way to actually produce great data that investors trust.
II. We're building the best financial toolkit for the AI age
Upon this data foundation is a whole range of Finbar products, built on the knowledge and experience of what professional investors actually need. This includes:
Machine-readable versions of millions of worldwide company documents
Fundamental time-series and datapoints on thousands of line items
Company primers and financial metadata
Prebuilt public company model XLSXs
A web platform for AI-automated financial data analysis, modelling, and report-writing
An Excel add-in to update any analyst model in seconds, and build any custom model in minutes
MCP and APIs to expose our fundamental data to programmatic and agentic users
And we already have a long list of items in development with institutional partners, including macro data, supply chains, newsflow, privates and more.
There’s still so much more we’ve got planned, but we’re genuinely so excited to start giving every investor the tools they need to make intelligent decisions in the AI age. So thanks for reading, and head to https://finbar.com to try us out. We’re looking forward to your thoughts!