We exist to help you do better financial work.
The data behind institutional analysis — accurate, point-in-time SEC fundamentals back to 1993 — costs five figures a year and hides behind black-box terminals. We rebuilt it from the primary source and opened it up: same depth, queryable from any AI agent, Python notebook, or browser. Cheap enough for one analyst. Honest enough for a backtest. Open enough to build on.
Valuein is founder-led and edge-native. The same people who build the data answer the support email — and we'd genuinely like to hear what would make your work better.
111M+
Standardized Financial Facts
19,000+
Companies Covered
1993
History Starts
18
Parquet Tables
Help anyone do better financial work — by making institutional-grade data and reasoning cheap, verifiable, and easy to use.
A world where every financial claim cites its source, every number is point-in-time correct, and good research compounds instead of getting lost in a folder of dead PDFs.
What we value
Honesty is the brand
Every number traces to a filing you can open. We are point-in-time correct with zero survivorship bias — and we tell you plainly what we don't do, not just what we do.
Built for your trust
Lineage-traceable, auditable, zero-retention. Your data and your model never train ours. You can verify our claims instead of taking them on faith.
Accessible by default
The data that used to cost five figures a year is free to start and $49/mo for the full universe. Better financial work shouldn't require an institutional budget.
Meet you where you work
Python SDK, MCP server, browser, or AI agent — same dataset, your choice of surface. We fit your workflow instead of forcing ours.
The builders answer the email
The people who ship the pipeline read every support message. Your feedback goes straight to the person who can act on it.
Why this exists
The financial data market is broken in three quiet ways. We built Valuein to fix all three.
It's too expensive.
Bloomberg starts at $24K/year per seat. WRDS academic licenses run six figures. Most “cheap” alternatives stop short of usable. Good data shouldn't require an institutional budget.
It carries survivorship bias.
Datasets that drop bankrupt and delisted companies make every backtest look 200 bps better than reality. A strategy that only works because Enron isn't in your data isn't a strategy.
It isn't point-in-time.
Most vendors silently overwrite amended filings. Query “Apple's 2018 revenue” and you get the restated value — not what the market saw on January 1, 2019. That one bug invalidates most published quant research.
Our fix is the same SEC EDGAR primary source everyone else uses, parsed and standardized into a queryable Parquet warehouse — with accepted_at on every fact and every delisted ticker preserved. Free for S&P500 history, $49/mo for the full universe.
How Valuein runs
Everything is automated and edge-native. The pipeline runs on schedule, ingests the latest SEC submissions, normalizes XBRL into ~280 canonical concepts, runs validation checks, and pushes Parquet to per-tier storage. The MCP server, Bulk Data API, and Python SDK all read from the same warehouse — no duplication, no drift.
The frontend, the API, and the rate limiter all run at the edge. No kubernetes cluster to babysit, no separate ops team, no hand-off between the people who built it and the people who maintain it. The authors who ship the pipeline answer your email.
That model has limits, and we'll say so plainly: Valuein isn't the place for white-glove account management or bespoke data-delivery contracts. What it offers instead is honest documentation, a real status page, public methodology, and a release cadence you can audit in the changelog. If those trades fit how you work, the free tier is one click away.
One dataset, your choice of surface.
Python quant, AI builder, or browsing analyst — Valuein meets you where you already work. Same data, same single token.
- Python SDK →
pip install valuein-sdk (or uv pip install valuein-sdk) — DuckDB-backed DataFrames in 60 seconds. SQL-native, no black-box abstractions, no API key required to start.
- MCP Server →
One Bearer token, every MCP-compatible client. Claude, Cursor, Codex, ChatGPT — all query SEC fundamentals as a first-class tool.
- Workspace →
An AI research workspace in the browser — bring your own LLM, build cited theses, and turn analysis into a durable, auditable record.
Tell us what would make your work better.
We're a small team, and the people who built this read every message. A missing concept, a slow query, a feature you wish existed, or a number that looks off — your feedback shapes what we ship next. There's no sales gauntlet; just a person on the other end who can actually do something about it.
Email us
Write to [email protected]. It reaches a person who can act on it, not a ticket queue.
Discord
Ask a question, share what you're building, or watch us ship in the open.
GitHub
Read the methodology, open an issue, or send a pull request. The data work is in the open.
X / Twitter
Follow along for forensic data tear-downs, product updates, and the founder journey.
Try it before you buy it.
The sample tier is one click — no signup, no credit card. S&P500 companies, 5 years of history, free forever.