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, high-impact agent actions staged for human approval, and reproducible runs — at under 5% of a terminal seat. The catastrophe this removes: an unverifiable number reaching a client, an IC, or an examiner.
- Every figure an agent shows is bound to its SEC filing — one-click verifiable, DDQ-ready.
- Human-on-the-loop by design: high-impact agent actions stage for a named human's approval and land in an immutable audit ledger.
- Managed runs execute at temperature 0 with a model allow-list — same inputs, same output, reproducible in an exam.
- Bring your own LLM; the key is sealed for 24h and never stored. Enterprise adds zero-retention.
Built for
Research & Finance Leaders
- Point-in-time accurate
- Survivorship-bias-free
- Every number cited to its filing
Works where you do
The pain points we remove
Adoption is solved — 2025-26 surveys put GenAI use at 95% of hedge funds. Trust is the bottleneck: your job is to deploy AI 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.
An answer nobody can defend
Unauditable output can't pass compliance, an investment committee, or a DDQ. AI is now the #1 compliance concern at RIAs — the exam letter arrives whether the workflow was clever or not.
Hallucinated numbers near client work
86% of CFOs say they've personally seen AI hallucinate finance figures. A model that free-types a number is one screenshot away from a credibility problem.
Autonomy your governance can't allow
Only ~5% of asset managers grant AI real autonomy — and they're right not to. What's missing isn't ambition; it's an approval gate and an audit trail that make delegation defensible.
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.
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.
The grind we take off your plate
From the daily check-ins to the month-end scramble — this is the recurring work Valuein automates so you spend your hours on the thesis, not the data.
Every day
- Field "can we use AI for this?" from the desk
- Watch that nothing unverifiable reaches a client or the IC
- Track adoption — and where analysts are getting stuck
Every week
- Review research quality and turnaround across the team
- Answer compliance and risk questions on AI and data use
- Brief leadership on coverage, capacity, and bottlenecks
Month-end & earnings
- Justify the data + tooling spend against the alternative
- Prep DDQ / audit materials on AI use and data lineage
- Plan headcount vs. automation for the next quarter
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.
Prove a human was on the loop
High-impact agent actions never auto-execute: they stage in an approval ledger, a named human approves or rejects, and the decision lands as an immutable audit entry with the fact_ids involved.
Answer "can you reproduce this?" with yes
Managed agent runs execute at temperature 0 against deterministic, typed tools and point-in-time snapshots — same inputs, same output, years later.
Pass a DDQ on AI use
Deterministic, tier-gated tools instead of a black box, an approval ledger, and fact-level lineage — a layered guarantee enforced by a build-gating CI test.
Keep your data and models confidential
BYO-LLM keys are sealed for 24 hours and never stored; Enterprise adds a full zero-retention tier.
Right-size the spend
Per-seat pricing across every channel at under 5% of a terminal — start free, then scale seat by seat.
Works where you do
One Bearer token reaches the same point-in-time data from your AI agent, your notebook, or your browser. Use the surface that fits the job.
Workspace
A governed research environment — BYO-LLM, cited reports, staged-action approvals, and a deterministic gate that blocks unbound numbers from exports.
ExploreMCP Server
Typed, tier-gated tools your analysts' existing AI clients call — every result auditable to its filing.
ExploreBulk Data API
Point-in-time Parquet for the data team — the same survivorship-free source feeding every desk.
ExploreAI you can put in front of a risk committee — every number bound to its filing, high-impact actions behind a human approval.
Institutional-grade, point-in-time data at under 5% of a terminal seat — billed per seat, starts free.
Your data and your model never train ours.
Frequently asked
How do you keep AI-generated numbers trustworthy?
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.
What does human-on-the-loop mean in practice?
Agent actions are risk-tiered. Read-only work runs freely; anything mutating or outward-facing stages in an approval ledger where a named human approves or rejects it, and every decision is recorded as an immutable audit entry. Nothing high-impact ships on a model's say-so.
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), a staged-action approval ledger with an immutable audit trail, 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.
Is it safe to just point our agents at any finance MCP server?
Not by default. A 2026 security sweep found 43% of public MCP servers carry at least one known vulnerability, and 5.5% ship poisoned tool descriptions. Ours isn't a wrapper around scraped HTML — every tool is typed, tier-gated, and returns lineage-tagged data, so there's no free-text surface for a prompt injection to exploit.
AI you can put in front of a risk committee — every number bound to its filing, high-impact actions behind a human approval.
111M+ standardized SEC facts across 19,000+ companies, 1993–present. Free to start — no credit card.