Connect SIGNAL to the systems that run your business.

What the SIGNAL integration layer is for

SIGNAL is the measurement and activation layer between brand activity and AI behavior. Its integration surface is designed for organizations that need the same intelligence visible in SIGNAL to participate in existing enterprise workflows: dashboards, warehouse pipelines, client reporting, internal agents, campaign operations and governance systems.

Getting started

A typical enterprise integration starts with the organization and measurement scope, not with a single isolated API call. SIGNAL needs to know what you are measuring, which digital properties belong to the organization, which brands/markets should be separated, and where the resulting data should be delivered.

1. Create or join your SIGNAL organization.
   Use the SIGNAL application to establish the organization that owns the measurement and integration context.
2. Define the entities you want to measure.
   Brands, products, competitors, markets, languages and digital properties become the stable context for intelligence, analytics and verification.
3. Connect first-party properties where required.
   Analytics and AI-access auditing depend on authorized data from the digital properties being measured.
4. Choose the data families your workflow needs.
   For example: answer intelligence for a BI dashboard, crawler observations for a data warehouse, or verification evidence for client reporting.
5. Provision integration access.
   Access is account-scoped. Your workspace provides the environment-specific authentication and permissions required for the approved integration.
6. Map SIGNAL objects into your downstream schema.
   Preserve entity IDs, observation timestamps, methodology/evidence references and dimensions so downstream analysis remains reproducible.
7. Validate before automating decisions.
   Confirm the intended market, model/source context, time window and evidence scope before using SIGNAL outputs in automated business logic.

Core concepts

Resource families

Access and authentication

SIGNAL integration access is scoped to an authorized organization. Production credentials are not embedded in public documentation and should never be placed in browser code, public repositories, screenshots or client-side analytics payloads.

• Server-side by default.
  Use integration credentials from trusted backend, serverless, data-platform or workflow environments rather than exposing them in public frontend code.
• Least privilege.
  Request only the data families and organization scope required for the integration.
• Separate environments.
  Keep development/testing and production credentials isolated when your account provides separate environments.
• Rotate and revoke.
  Treat credentials as operational secrets and replace them when access changes or exposure is suspected.

The exact credential format and authorization scopes are supplied with provisioned API access. This public guide deliberately does not fabricate a universal header name or token format.

Data model: keep context with the number

AI measurement is easy to misuse if a downstream system keeps the score but drops the context. Integrations should preserve enough metadata to answer: what was measured, for whom, where, when, under which methodology, and with what supporting evidence?

Common integration workflows

Executive AI visibility dashboard

Combine category demand, brand visibility, share of voice, recommendations, citation/source influence and competitive changes into an executive BI view. Use stable entity and market dimensions so period-over-period changes remain comparable.

AI crawler → referral → outcome analysis

Join first-party crawler/agent observations with AI referrals and business outcomes to understand the relationship between machine access and downstream human discovery. Do not assume a crawler request is itself a conversion or direct attribution event.

Independent agency/campaign evidence

Pull baseline and post-campaign verification outputs into client reporting while preserving methodology, evidence and confidence context. This keeps vendor-reported performance separate from independently measured results.

Agent-ready information operations

Coordinate authoritative information prepared for publisher, Web, API, MCP, A2A, Mobile and TRUYN delivery with provenance and citation monitoring. Distribution is the information supply layer; measurement remains connected through Intelligence and Analytics.

Evidence, provenance and reproducibility

SIGNAL Verify is designed around independent evidence rather than self-reported success. When integration consumers use verified outputs, they should retain the identifiers and methodology context necessary to trace a reported conclusion back to its supporting evidence.

Integration and delivery patterns

SIGNAL participates in several machine-readable delivery patterns. The correct pattern depends on whether your system is consuming measurement data, receiving event-style updates, building a governed internal dashboard, or exposing authoritative information to software agents.

Web, API, MCP, A2A, Mobile and TRUYN are part of the current SIGNAL Distribution delivery model. Availability and account scope depend on the product configuration provisioned to the customer.

Illustrative data examples

The examples below show the shape of information an enterprise integration should preserve. They are intentionally not presented as undocumented production endpoint contracts.

{
  "entity": { "type": "brand", "name": "Example Brand" },
  "market": "example-market",
  "period": { "from": "…", "to": "…" },
  "measurement": {
    "metric": "ai_visibility",
    "value": 72.4,
    "comparison": { "direction": "up", "change": 8.7 }
  },
  "context": {
    "intent": "enterprise purchase intent",
    "methodology": "versioned",
    "evidence": "reference retained"
  }
}

{
  "audit": "campaign-or-ai-access",
  "entity": "Example Brand",
  "comparison": {
    "baseline": "retained",
    "post_period": "retained"
  },
  "result": {
    "classification": "independently_verified",
    "confidence": "retained when applicable"
  },
  "methodology_version": "retained",
  "evidence_reference": "retained"
}

Governance and security expectations

• Keep tenant boundaries explicit.
  Do not mix brands, subsidiaries, clients or markets merely because they share one reporting destination.
• Keep provenance attached.
  When data leaves SIGNAL, retain the fields required to explain where a result came from and how it was measured.
• Separate measurement from activation permissions.
  A team allowed to read analytics should not automatically gain authority to change media or distribution operations.
• Protect credentials and exported evidence.
  API secrets belong in secret-management systems; evidence may contain business-sensitive information and should follow your organization's access policy.