AI and integrations
How to make competitive intelligence available inside an AI assistant
A practical data-contract and governance guide for exposing competitor activity to an AI assistant without sacrificing source evidence or access control.
An AI assistant can make competitive intelligence easier to query, but only if the underlying data contract is explicit. The assistant needs bounded records with stable identifiers, source links, timestamps, and clear distinctions between observations and interpretations.
Design the response around evidence
A useful activity record contains competitor, page title, page URL, observed change, detected time, importance, confidence, and source evidence. Keep inferred reason in a separate field. This lets the assistant answer “what changed?” without silently blending in “why it may matter.”
Do not expose every internal field. Return the smallest payload that supports the user’s question. Bounded output reduces accidental disclosure and makes model responses easier to audit.
| Field | Purpose | Example |
|---|---|---|
| Competitor | Identify source | Acme |
| Observed change | State the fact | Annual plan added |
| Source URL | Enable verification | Pricing page |
| Time range | Keep answer current | Last 7 days |
| Interpretation | Optional hypothesis | May support annual conversion |
Enforce scope at the server
The user’s selected organization is not an authorization boundary by itself. Resolve the authenticated identity, verify membership, and constrain every tool call to that organization. The MCP authorization guidance provides protocol context, but membership policy remains application logic.
Add rate limits, pagination, maximum date ranges, and response-size caps. These controls protect both the connected client and the source system from accidental broad queries.
Make the assistant honest about uncertainty
Prompting should require the assistant to say when evidence is missing, distinguish an observation from an inference, and link the source for material claims. Do not ask it to guess the competitor’s private intent from one public edit.
The facts versus interpretation guide can serve as the editorial contract for AI responses as well as human briefs.
Design the answer contract first
Before connecting an assistant, define the smallest answer a user needs: competitor, changed page, observed change, date, source link, and confidence. A stable contract helps the assistant cite evidence and makes it easier to test regressions. It also limits accidental exposure of raw page content and internal prompts.
Prefer typed, bounded tool inputs such as competitor ID, time window, page type, and maximum results. The MCP tools specification provides the protocol shape, but the application's authorization and data minimization rules still belong on the server.
| Need | Input | Output |
|---|---|---|
| What changed? | Competitor and period | Activity summaries |
| Where? | Activity ID | Page title and source URL |
| How sure? | Evidence request | Observed versus inferred fields |
Make authorization visible and narrow
Use OAuth authorization with PKCE, explicit consent, and scopes that describe the read-only capability. The MCP authorization guidance explains the relevant discovery and authorization flow. Bind each grant to one client and organization, then recheck membership at every tool call.
Explain what the assistant can access, what it cannot access, and how to revoke it. Access and refresh token lifetimes should be documented and tested; token rotation and revocation are security behavior, not implementation details to leave implicit.
Test retrieval, citations, and failure
Build fixtures for no results, stale scans, duplicate activities, long titles, inaccessible source pages, expired tokens, and revoked organization access. Verify that the assistant says when evidence is unavailable. A graceful limitation is more trustworthy than a plausible unsupported answer.
Measure answer quality with a predeclared evaluation plan: citation presence, factual agreement with stored evidence, scope correctness, and refusal behavior for unsupported requests. Until that evaluation runs, describe quality improvements as hypotheses rather than results. Internal guidance on monitoring website changes can define the evidence baseline.
