Valuein
Auditable SEC financial data your AI agent can't hallucinate
Valuein launched on June 30, 2026 with 4 votes, #3,322 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Equity research and financial analysis, minus the grind. Valuein is the data + intelligence layer that makes analysts, researchers and quants effective on day one: screen, value, monitor and write the memo without fighting your data. Survivorship-free, PIT SEC facts + smart-money signals for 19,000+ US companies since 1993. Plug into your stack: AI agents (Claude/Cursor) via MCP 75 tools + 27 analyst workflows, the Workspace, or Python. Every number traces to its filing, no AI hallucinations.
111M+ point-in-time, survivorship-free SEC EDGAR facts built for AI agents. Every number traces to its filing. MCP-first — works in the AI you already use.
Build your analysis, valuation models, reports, and verify every number in minutes. Share them with your team or clients. Works in your browser, from Python , or inside the AI you already use . Typed tools return SEC-filed values; the model only arranges the words. Same inputs, same output — and every figure is one click from its filing. There is no free-text digit to invent — the model selects a fact, it doesn't author one. temperature 0 · point-in-time snapshots · deterministic tools. Research your firm can reproduce years later. No re-running, no trust-me. An auditor clicks through and checks the figure against the original 10-K. We didn't ask the model to stop hallucinating. We removed…from valuein.biz
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FactIQ – a realtime econ+finance database for AI agentsJul 2026 · github.com · ▲7Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…

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