Platform & Integration

The AI visibility layer built to be verified against.

Signal Scanner is the score you cannot fake, because we do not do the work. Every number traces to a stored engine response. That makes it the one measurement a platform can embed and trust.

The tool doing the work is not the tool that should grade the work.

Every automation and optimization platform has the same structural blind spot: the score it reports is produced by the same system that produced the work. Signal Scanner is different by design. We never touch your site. We ask the six AI engines directly, record the raw responses, and score from those. That separation is what makes the number useful to an outside platform.

Real measurement, not estimates

Every score traces to a stored engine response you can read. Nothing is inferred, modeled, or extrapolated.

A schema-versioned API

Pull the Signal Score, citations, and the fix file as structured JSON. The schema carries a version field so your integration can detect format changes before they land.

You do the work. We verify it.

Trigger a rescan and get an independent read on whether the engines actually moved. The verdict comes from the engines, not from us.

The fix file as structured data.

Every brand gets a fix file: a list of the specific changes that would move the Signal Score. At ?format=json, it comes back as a schema-versioned document with stable item IDs, executor labels (CODE or MANUAL), supporting evidence from the engine responses, and ready-to-paste code blocks where they apply.

A platform can read this file, route CODE items to a deployment workflow and MANUAL items to a task queue, trigger a rescan, and compare before and after scores automatically.

GET /api/connector/fix-file/:brandId?format=json

GET /api/connector/fix-file/:brandId?format=json

{
  "schema_version": "1.0",
  "brand": "example.com",
  "generated_at": "2026-07-16T06:00:00Z",
  "items": [
    {
      "id": "nap-consistency-001",
      "title": "Align NAP across all citation sources",
      "executor": "MANUAL",
      "priority": "high",
      "evidence": "Name mismatch found on 3 of 6 engines",
      "code": null
    },
    {
      "id": "schema-org-localBusiness-001",
      "title": "Add LocalBusiness schema to homepage",
      "executor": "CODE",
      "priority": "high",
      "evidence": "No structured data detected in engine responses",
      "code": "<script type=\"application/ld+json\">...</script>"
    }
  ]
}

What the API gives you

GET /api/connector/signals/:brandId

The composite Signal Score and the per-signal breakdown that produced it.

GET /api/connector/fix-file/:brandId

The prioritized fix list in markdown or, with ?format=json, as a schema-versioned structured document.

GET /api/connector/citations/:brandId

The citation domains that appeared in engine responses, with frequency and source detail.

GET /api/connector/reputation/:brandId

Reputation signals extracted from engine responses across the last scan.

POST /api/connector/rescan/:brandId

Trigger a fresh measurement. Call before and after to measure the impact of a change.

POST /mcp

Model Context Protocol endpoint for AI assistant integrations. Exposes the same data as structured tool calls.

All endpoints are authenticated with an API key. Access requires a Signal Scanner plan.

Ready to build on verified AI visibility scores?

API access is set up through our team. Reach out and we will walk through the integration with you.