Technical case: evidence-first UK company verification in Relevance AI
Problem
An AI agent can call an API, but that does not mean it knows whether the source is healthy, the company identity is unambiguous, the evidence supports the intended action, or a payment is authorized. A generic successful HTTP call can hide uncertainty and encourage an agent to continue too far.
Integration design
RunOnProof is exposed through a Relevance AI API Tool step. The first Tool verifies a UK Companies House company number through the Company Check solution. Repository-owned selection and normalization rules turn the canonical response into PROCEED, REVIEW, or STOP while retaining evidence, coverage, freshness, limitations, and economic activity.
The API Tool mechanism was selected because Relevance AI officially documents custom API steps. The production RunOnProof MCP available when the integration was designed used a legacy contract rather than the public remote Streamable HTTP server required by Relevance AI’s MCP Client. This decision must be reevaluated if the certified MCP surface changes.
Request contract
The primary business inputs are the exact company_number and an idempotency_key for the exact request. The Tool performs:
POST https://api.runonproof.com/v1/gb/solutions/company-check
It sends JSON headers, binds the idempotency key, accepts JSON, and leaves 4xx/5xx available to the normalization step. Automatic retry is disabled for the paid POST.
Economic boundary
The public contract export documents gb.solution.company_check.v1 at 0.04 USDC. An unpaid request can return HTTP 402. The Tool maps 402 to REVIEW, displays the product and price, and stops. It has no wallet or payment proof.
Decision safety
Only a canonical allow result with healthy required sources can map to PROCEED. Ambiguity, missing evidence, outage, stale source, hold, retry, no-decision, or payment boundary maps to REVIEW. Denied, blocked, prohibited, and unsupported actions do not continue.
No-match is explicitly limited by list, identity match, policy, and observation time. Company registration evidence is not used to claim bank ownership, invoice authenticity, complete beneficial ownership, universal licensing, solvency, or absence of all risk.
Reproducibility
The repository includes a versioned authoring manifest, JSON schemas, deterministic runtime, system prompt, seven synthetic fixtures, unit tests, capability matrix, support research, installation guides, and rollback instructions.
npm run demo:relevance-ai
npm run test:relevance-ai
The synthetic demo has an economic delta of 0.00 USDC and makes no network call.
Publicly reproducible result
The repository runs seven synthetic scenarios. The quote fixture shows the correct product and 0.04 USDC, returns REVIEW, redacts payment material, and records zero production calls and payment attempts.
Limitations
Repository CI evidence is synthetic and does not reveal or certify a private workspace installation. Runtime health and price must be read at execution time. Relevance AI UI and platform behavior can change independently of this repository.
Why it matters
The case demonstrates that an agent integration can make uncertainty and cost explicit before a consequential action. The useful product is not merely an API response; it is a narrow, auditable permission boundary with evidence attached.