Every AI ends with a disclaimer

AI makes you check its work.
Raptor checks its own.

Every AI tool hands you an answer and the same fine print: “can make mistakes — please double-check.” It puts the burden on you. Raptor gives you the answer plus what it checked, what it found, and proof you can verify yourself — and it flags, in plain language, the parts it couldn’t confirm.

See it check its work Verify one yourself
No account needed. Check a real AI decision in your browser — then try to tamper with it.
Where Raptor sits

Three ways to deal with AI mistakes. One actually catches them.

Observability

Tells you what happened after.

Logs, traces, dashboards. Useful for debugging. Useless when you need to know whether the answer in front of you is right.

Evals

Tests capability against known cases.

Measures whether the model can get it right. Doesn't enforce that it must, or prove that it did, in production.

Raptor

Checks its own work — and proves it.

Every answer is checked against its evidence, labeled by how certain it is, and backed by proof you can verify yourself. It flags what it couldn't confirm instead of hiding it.

How Raptor works

The AI answers. Raptor checks the answer.

The AI does what AI is good at — understanding your question and drafting an answer. Then a second layer does the part AI can't be trusted to do on its own: it checks that draft against the evidence, labels how solid each part is, holds any real-world action for your approval, and produces a result you can prove after the fact. The AI proposes; the checking layer decides what actually stands.

Proposal Layer

  • AI interprets fuzzy input
  • AI generates candidates
  • AI infers intent
  • Full upstream flexibility
→

Commit Layer

  • Rules validate evidence
  • Governance applies
  • Trust boundaries mark output
  • The output users act on

01 Every claim is labeled by how sure it is

Each part of the answer is tagged with how it was checked — CONFIRMED EXECUTED RETRIEVED INFERRED UNKNOWN. You see what it verified versus what it only guessed, inline, instead of a wall of equally-confident text.

02 Every action asks before it acts

When the AI wants to do something, not just say something, Raptor stops and asks first. Nothing runs silently. You see what it's about to do, why, and the risk if it's wrong — before it happens.

03 The proof holds up to anyone

Every answer carries a receipt you — or a customer, or a regulator — can check independently, without taking Raptor's word for it. Same rigor every time, provable after the fact.

What it looks like

Watch it check its own work.

Here's a real answer, checked. Every part is labeled by how it was verified — confirmed, sourced, or just inferred. The action it wants to take is held for approval before it runs. And the whole thing carries a receipt you can verify independently. Nothing certain and nothing guessed look the same.

User query

What's the current authorization status for Project Sentinel, and can you extend the Phase 2 deadline to March 15?

Raptor-checked response
CONFIRMED basis: cryptographic_verification

Project Sentinel (ID: PRJ-2847) is currently authorized through Phase 2 with an active status. Authorization was granted on 2026-01-12 by ISSM J. Torres.

RETRIEVED basis: file_content

The current Phase 2 deadline is February 28, 2026. Three deliverables remain open against this phase.

EXECUTED Confirmation required

Proposed action: Extend Phase 2 deadline from 2026-02-28 to 2026-03-15 for Project Sentinel.

Confidence: RECOMMENDED — good confidence, solid evidence.

Basis: Deadline extensions require project-lead or higher authorization. Current actor has project-lead grant.

Risk if wrong: Downstream milestone dependencies may shift. Three open deliverables inherit new deadline.

Approve Reject
INFERRED basis: model_response

Based on current velocity, the three open deliverables are likely completable within the extended window. This assessment is probabilistic and not verified against resource allocations.

Response provenance ↓
execution_id: 7f3a2c91-...
intent_id: a8b4e1d0-...
input_hash: sha256:e3b0c44...
correlation_id: 9c1d4f82-...

Every checked answer carries cryptographic provenance — verifiable, replayable, immutable.

The CONFIRMED line can't appear unless it was cryptographically verified. The EXECUTED action can't run until someone approves it. The INFERRED line is flagged, out loud, as the model guessing — the part you'd otherwise have to catch yourself. You see exactly what it verified, what it did, and what it couldn't confirm. Response structure simplified for illustration. Full schema in architecture docs.
Verified state

What's actually built.

4
AI providers, unified
85
REST API routes
22
Immutable Postgres tables
100%
Cross-model eval accuracy
  • 4 production-class AI providers behind a unified interface — Anthropic, OpenAI, Gemini, Together AI
  • OpenAPI spec and generated TypeScript SDK with trust-boundary types preserved
  • 9-unit UI design system implemented in Preact and deployed
  • 22 immutable Postgres tables with database-level append-only enforcement — every governed response is recoverable for audit
  • Verifiable human-oversight receipts (EU AI Act Art. 14 / M-25-21 Practice 5) — a human approval minted as a receipt a regulator can prove independently, with a configurable pre-execution approval gate
  • Public, account-free receipt verification — anyone can prove a receipt against a signed, on-chain-anchorable Merkle root at /verify, recomputing the proof and signature in their own browser, without trusting Raptor
  • Cross-model eval: Llama 3.3 70B and Claude Sonnet both at 100% accuracy on a 20-scenario decision truth set — open-weight bridge empirically validated
  • MCP server functional with token auth (OAuth 2.1 designed)
  • SDVOSB certified for defense and government set-aside contracting
What's not built: self-hosted deployment, MCP registry publication. What is: pricing is locked, 14-day Workspace trial is live, pay-as-you-go Infrastructure access is available, and verifiable human-oversight receipts with public, account-free verification are live. We're early. The substrate is real.
The category

Making AI check itself isn't a feature. It's a category.

The cost of an unchecked AI mistake is highest where being wrong is expensive — SR 11-7 in finance, HIPAA in healthcare, IL4/IL5 in defense, the EU AI Act broadly. Those industries already require audits that probabilistic-everywhere AI can't satisfy, and public failures of unchecked AI in production are visible across all of them. Adjacent tools tell you what happened or test whether the model can get it right — they don't check the answer in front of you or prove it.

The position — AI that checks its own work and proves it — is open. Whoever ships the first credible version that wins a real buyer cohort defines the language the category uses.

Go deeper

Five audiences. Different sections.

Running AI in production?

If you've had an incident you couldn't explain to a customer or auditor, you're who Raptor is built for.

Read the buyer's view

Building AI products?

Three integration patterns, four model providers, an SDK and an MCP server. Read the code, then decide.

Read the developer's view

Model provider, integration partner, or channel?

Raptor's substrate is multi-provider by design. The right partnership shape depends on what you bring.

Read the partner's view

Evaluating substrate plays?

Self-funded, pre-revenue, solo founder, fully built substrate. Honest about all four. Read what we're asking you to evaluate.

Read the investor's view
Next step

Don't take our word for it. Check it yourself.

Watch Raptor catch a real AI mistake, or verify a real decision in your browser — no account. Then, if you're running AI where being wrong has a cost, talk to the founder, not a sales engineer.

See it check its work Talk to the founder

14-day Workspace trial · pay-as-you-go Infrastructure at $0.012/response · pricing details