Commerce · AI visibility

Agent Visibility Optimisation (AVO)

AVO structures product data so AI agents can discover, accurately represent, compare, and recommend products. It is distinct from GEO, which targets AI citations, and SEO, which targets page rankings. AVO targets agentic purchase decisions.

01

The operational problem

Product data built for search often fails when an AI agent becomes the buyer interface. Keyword-stuffed titles, thin descriptions, stale price fields, and missing compatibility attributes make products harder for agents to compare accurately.

Commerce teams usually optimise for humans landing on a PDP. Agentic commerce adds another customer: the AI system that must decide whether a product is relevant, in stock, compatible, substitutable, and safe to recommend.

The risk is commercial and operational. If product records are incomplete, an agent can omit the brand, recommend the wrong alternative, hallucinate compatibility, or push a purchase path that a governed assistant should have blocked.

Proof artifact

Prompt-panel evidence sheet

Product evidence gaps block or escalate recommendations before a commerce agent uses them.

Download AVO-ready resources →
02

Governed workflow

  1. Run a prompt panel across 30 buyer and operator prompts on five AI engines, tracking whether the brand appears, whether the product is represented accurately, and whether competitors are recommended instead.
  2. Analyse citations, product mentions, price freshness, substitute recommendations, accessory logic, and answer confidence for each target SKU family.
  3. Parse catalogue, feed, PDP, schema.org, and merchant-centre attributes for completeness, consistency, freshness, compatibility, and evidence quality.
  4. Score gaps through PRISM's S-dimension so visibility becomes a governed readiness metric rather than a marketing opinion.
  5. Remediate the top SKU set, rebuild the product knowledge graph, and re-test the prompt panel to show before/after AVO delta.
03

HARNEXA Harness controls

  • AVO feeds HARNEXA Harness by improving the grounded product context available before PHANTOM or a commerce assistant proposes an action.
  • Recommendation quality can be held behind confidence floors so incomplete product evidence blocks or escalates a suggestion instead of silently reaching the buyer.
  • AVO score becomes a PHANTOM pre-flight check for product recommendations that rely on catalogue, stock, substitute, accessory, or compatibility data.
  • Every remediation step is tied to the source field, owning system, freshness rule, and audit evidence needed for AgentOps review.
04

Protocol readiness lab

Prepare for MCP and UCP without exposing execution.

AVO is the first step toward agentic commerce protocols. Score the product evidence, data freshness, tool boundary, transaction boundary, and protocol scope before any public MCP, UCP, ACP, AP2, checkout, or payment surface is considered.

Product evidence

Can buying agents understand, compare, and recommend the product accurately?

Commerce freshness

Are inventory, price, availability, and fulfilment constraints current enough?

Tool boundary

Can internal tools be exposed to an agent without creating shadow access?

Transaction boundary

Where does recommendation stop and commercial action begin?

Protocol scope

Which commerce protocol work is appropriate now?

CLEAR scorecard

Measured before it scales.

Citation share% queries

How often the brand or product appears across the prompt panel.

Attribute completion99.9% target

SKU records reach the Golden Record threshold for agent-readable attributes.

Recommendation accuracyGrounded

AI engines recommend the correct product, substitute, or accessory with current evidence.

AVO deltaBefore/after

Measured improvement after enrichment, graph updates, and prompt-panel re-test.

Golden Record threshold

3-4xGoogle Merchant Center data indicates 3-4x improvement in AI visibility for products reaching 99.9% attribute completion. HARNEXA translates that visibility work into governed commerce-agent readiness.

Sprint output

What the client receives

  • AVO audit report across prompt panel, citation analysis, catalogue quality, and agent-readiness gaps
  • Enriched product feed for the top 500 SKUs or the agreed priority assortment
  • Product knowledge graph in pgvector with attributes, substitutes, accessories, compatibility, and freshness metadata
  • AVO before/after report showing citation share, recommendation accuracy, and PRISM S-dimension movement
  • Feed governance specification covering owners, update cadence, validation rules, and AgentOps review points

Operator FAQ

Questions buyers ask before scoping.

Ready to scope this workflow with governance from day one?

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