Evidence that moves products forward

Give your team the evidence and the next move.

See which products need attention, what is holding them back, and who can fix it. Your CEI assessment brings the scorecard, product-level evidence, and prioritized action plan together, so leaders can set priorities and teams can move straight into the work.

PARTIALLY READY
SAMPLE CEI SCORECARD68/100FoundationDifferentiationRetrievalSKU-4471 · 3 of 7 attrs missingSKU-9920 · retrieval miss x2SKU-1180 · unsupported claimPARTIALLY READY
What you receive

Give every team the evidence and next move it needs.

Executive Report

A leadership-ready view of the CEI score, readiness band, competitive context, commercial opportunity, and prioritized recommendations.

Technical Evidence Package

Signal-level data, product-level findings, crawler diagnostics, retrieval results, and source evidence.

Remediation Roadmap

A prioritized plan mapping each fix to business impact, owner, effort, and expected readiness improvement.

Activate Artifacts

Structured data recommendations, attribute files, terminology maps, and content briefs when Activate is in scope.

Why this product, why this signal, why now

Representative findings from a CEI assessment.

Attribute completeness

A high-share category is missing material, fit, size, compatibility, or use-case attributes across many SKUs.

Differentiation strength

Multiple products use boilerplate descriptions, making them difficult for AI to distinguish.

Retrieval quality

Realistic shopping queries surface the wrong product because constraints are missing or inconsistent.

Illustrative example

What one fixable gap looks like, end to end.

A worked illustration of how a single finding moves from diagnosis to measurable readiness.

Query

"best waterproof hiking boots under $150 with a wide fit"

Finding

A large share of otherwise-eligible products are missing width or waterproof attributes, so AI cannot confirm they meet the constraint and leaves them off the shortlist.

Fix

Targeted PIM attribute fills plus JSON-LD enhancement on the affected product pages, delivered as ready-to-apply artifacts.

Result

Those products become eligible for constraint-based AI queries they were previously excluded from, and the change is re-scored against the baseline.

Example shopper request: Waterproof hiking boots with a wide fit, under $150.

Before the catalog fix

  • Waterproof: not recorded
  • Wide fit: not recorded
  • Price: $139

Two of the shopper's three requirements cannot be confirmed from the catalog.

After the catalog fix

  • Waterproof: evidence published
  • Wide fit: evidence published
  • Price: $139

All three requirements are clear, helping the product compete for the shortlist.

Recommendation, choice, and growth

See how evidence becomes recommendation, choice, and growth.

See where AI already understands and trusts your products, where the next opportunities sit, and which evidence-backed fixes can strengthen the shortlist.

Get a preliminary catalog check