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ValueinValuein
For CFOs, Heads of Research & CIOs

AI your team can use — and your risk committee can sign off on.

Give analysts an AI research layer wired to SEC-filed data: every number auditable to its filing, your model and data never train ours, and the whole thing costs under 5% of a terminal seat.

  • Every figure an agent shows is bound to its SEC filing — one-click verifiable, DDQ-ready.
  • Bring your own LLM; the key is sealed for 24h and never stored. Enterprise adds zero-retention.
  • Per-seat pricing at under 5% of a terminal — no rip-and-replace, no AI team to hire.
  • Point-in-time and survivorship-free, so research and backtests can't quietly cheat.

Built for

Research & Finance Leaders

  • Point-in-time accurate
  • Survivorship-bias-free
  • Every number cited to its filing

Works where you do

WorkspaceMCP ServerBulk Data API
Recommended plan
Institutional
86%
of leaders say accuracy blocks AI agents
~13%
of finance teams run agentic AI today
<5%
of a terminal seat

The pain points we remove

Your team wants AI. Your job is to deploy it without a wrong number reaching a client, an IC, or a regulator — and without a budget line that rivals a terminal. These are the blockers Valuein removes at the source.

1

An answer nobody can defend

86% of enterprise leaders name reliability and accuracy as the top blocker to AI agents. Unauditable output can't pass compliance, an investment committee, or a DDQ.

2

Hallucinated numbers near client work

A model that free-types a figure is one screenshot away from a credibility problem. The risk isn't the workflow — it's the unverifiable digit sitting inside it.

3

Pilots that never reach production

Only about 13% of finance teams run agentic AI; most stall on trust, confidentiality, or a missing in-house AI team. The hard parts shouldn't be your project.

4

Your data and model leaking to a vendor

If your edge or your clients' data trains someone else's model, the deal dies in security review. Confidentiality is non-negotiable.

5

A budget that rivals a terminal

Institutional-grade data shouldn't cost institutional-terminal money — or lock you into a multi-year, per-seat contract you can't right-size.

What you can do with Valuein

Each job you need done, mapped to the exact capability that delivers it.

Prove every number an agent surfaces

verify_fact_lineage round-trips any figure back to its SEC filing; derived numbers carry their formula and input fact_ids.

MCP · Datasets

Pass a DDQ on AI use

Deterministic, tier-gated tools instead of a black box — a layered guarantee enforced by a build-gating CI test.

Trust architecture

Keep your data and models confidential

BYO-LLM keys are sealed for 24 hours and never stored; Enterprise adds a full zero-retention tier.

Workspace · Enterprise

Deploy without hiring an AI team

Connect the AI clients your people already use in about a minute — no RAG to build, no scraping to maintain.

MCP · Workspace

Right-size the spend

Per-seat pricing across every channel at under 5% of a terminal — start free, then scale seat by seat.

One token · Pricing

Frequently asked

How do you stop the AI from hallucinating numbers?

Tools return the figures; the model only arranges the words — it is never the source of a digit. Every value carries a deterministic fact_id and a clickable SEC EDGAR link, and in the Workspace a number that can't be traced is blocked from the exported report. A build-gating CI test fails if that guarantee ever breaks.

Will this pass our DDQ and compliance review?

That's the design goal. You get point-in-time, survivorship-free data, provenance on every figure, typed tier-gated tools (not a black box), human-in-the-loop by default, and BYO-LLM with no key storage. Enterprise adds a zero-retention tier and dedicated infrastructure.

How is pricing structured?

Per seat, one token across MCP, Workspace, SDK, and Bulk Data API. Pro is $49/seat/mo and Institutional $499/seat/mo — under 5% of a terminal seat. Start free on the S&P 500 with no card and scale by seat; Enterprise is a custom contract when you need dedicated infrastructure.

Do our analysts need to be AI experts?

No. They ask in plain English in the tools they already use; the hard parts — data, governance, provenance — are the product. No RAG to build, no prompt engineering, no AI hires.

Can we keep our own LLM and data private?

Yes. Bring your own Anthropic or OpenAI key — it's sealed in a 24-hour cookie and never stored or trained on. Enterprise adds a full zero-retention option.

AI you can put in front of a risk committee — every number bound to its filing, the unbound ones blocked.

111M+ standardized SEC facts across 19,000+ companies, 1993–present. Free to start — no credit card.