CONVERG3NCE

CONVERG3NCE audits a brand's web presence the way an AI shopping agent actually reads it, scores how agent-ready the brand is, and says what to fix.

As LLMs answer "what should I buy" directly, brands become invisible to a channel they cannot see or control. These docs describe the machinery that measures that channel: the audit pipeline, the Agent-Readiness score, and the public MCP server an agent can query directly.

The loop

AUDIT ──▶ DIAGNOSE ──▶ SCORE ──▶ FIX ──▶ DEFEND
  │           │          │        │        │
  │           │          │        │        └─ rerun monthly, store everything
  │           │          │        └────────── prioritized fixes, ranked by impact
  │           │          └─────────────────── deterministic 0–100, no LLM
  │           └────────────────────────────── LLM judge reads each response
  └────────────────────────────────────────── query 4 model families with tailored questions

Start here

What is documented, and what is not

Everything on this site is derived from the running system — the audit engine's own source, its scoring arithmetic, and measurements taken against real brands. Where a number is uncertain, the uncertainty is stated rather than smoothed over. See Measurement reliability for the honest limits of the score.

This site does not document internal operations, client engagements, or the operator dashboard. Those are not public surfaces.