Your AI works in production. The audit trail doesn't.
Raptor is the governance layer for regulated-industry AI workloads. If you've had an incident you couldn't explain to a customer or auditor, this page is for you.
You probably have one of these problems right now.
- Logging raw model outputs to S3 and hoping the logs are enough if someone asks
- Reviewing AI-taken actions after the fact and manually marking which ones were correct
- Writing prompt-engineering guardrails and discovering them defeated in production
- Telling your compliance team "we can pull logs if needed" without a real answer for what those logs prove
- Explaining to a customer why the AI told them something incorrect, and being unable to point to which source the answer came from
The shared shape: probabilistic-everywhere architecture cannot make process-level claims about what your system did. You can produce outputs, but you cannot prove which evidence was considered, which rules applied, or what the system was permitted to do.
Raptor inverts the architecture.
The AI does what AI is good at — interpreting fuzzy input, generating candidates, inferring intent. The deterministic layer does what rules are good at — validating those candidates against your evidence requirements, applying your governance rules in the same order every time, and producing the output your users act on.
When an auditor asks what your AI did, you have an answer that's evidence-backed, not screenshot-backed.
What you get.
| Capability | What it does |
|---|---|
| Trust-boundary metadata | Every response segment is marked CONFIRMED EXECUTED RETRIEVED INFERRED UNKNOWN — visible inline, expandable to full provenance |
| Confirmation-gated actions | The AI proposes; you (or your user) approve before anything mutates state |
| Multi-provider abstraction | Anthropic, OpenAI, Gemini, Together AI all behind a unified Provider interface — swap models without rewriting your governance |
| Hash-chained audit log | execution_events with cryptographic chain — replay any session, verify integrity, prove what happened |
| Temporal trust lifecycle | Policy-level supersession enforced (append-only, never overwritten). Claim-level freshness tracking designed and rendered; backend lifecycle automation in active development |
| Decision support | Recommendations come with explicit confidence (CLEAR / RECOMMENDED / ADVISORY / INSUFFICIENT), basis, and risk-if-wrong. Frontend complete; backend confidence mapping in refinement |
| MCP server | Your existing AI assistants can invoke Raptor's governed capabilities via the Model Context Protocol. OAuth 2.1 designed; token auth available for evaluation |
| TypeScript SDK | Integrate Raptor into your product with typed clients and full trust-boundary types preserved |
Worth saying directly so you can rule us in or out fast.
- Not a model. Raptor calls models you already use. We're orthogonal to model choice.
- Not a chatbot. Raptor has an end-user chat product as the canonical demonstration of the substrate; the substrate is what other products integrate.
- Not an observability tool. Observability tells you what happened after. Raptor governs what's permitted to happen.
- Not enterprise-procurement-ready. No glossy slide deck, no reference customer list, no procurement playbook. If you need those before evaluating, we're early for you.
- Not self-hosted yet. Current deployment is SaaS. Self-hosting and BYOC are on the architectural roadmap.
- Priced and transparent. Workspace tiers from $250/month (Team) to $999/month (Growth) with a 14-day trial (credit card required, auto-converts on day 15). Infrastructure pay-as-you-go at $0.012 per governed response. Ask for the full pricing spec.
Five questions. Two or three "yes" answers and we should talk.
- Are you running AI workloads in production today?
- Have you had an incident where you couldn't explain or audit what the AI did?
- Is someone on your team responsible for AI governance or compliance?
- Are you building multi-step or autonomous agent workflows?
- Would you be the person to try a new platform, or would someone else need to approve?
Next step.
If two or three answers were yes, the next step is a 30-minute conversation with the founder. Not a sales engineer. The first conversation is technical: your AI workload, the specific governance problem you're solving, whether Raptor solves it at this stage.
info@harpyits.com