The audit is just the entry point. We develop the product data, feed connections and update workflows that help discovery systems understand your catalogue. Channel eligibility and recommendations depend on the platform; we explain those boundaries before implementation.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01A catalogue that machines can understand
What you may be seeing
- Product facts disagree across pages, feeds and internal systems.
- Variants, specifications and availability are incomplete.
- Structured data falls behind catalogue changes.
We audit catalogue attributes, product markup and supported feeds against the source records. We develop validation and synchronisation jobs so approved descriptions, variants, prices and availability stay consistent. This strengthens the data foundation for search and AI discovery; it does not guarantee inclusion, ranking or a recommendation.
Implementation & verification
We audit catalogue attributes, product markup and supported feeds against the source records. We develop validation and synchronisation jobs so approved descriptions, variants, prices and availability stay consistent. This strengthens the data foundation for search and AI discovery; it does not guarantee inclusion, ranking or a recommendation.
How we verify the work
- Map authoritative fields and the systems that publish them.
- Implement structured product data and feed validation.
- Monitor freshness, errors and permitted discovery surfaces.
02A practical route into AI discovery
What you may be seeing
- You are unsure which emerging channels suit your product.
- An AI visibility claim is difficult to verify.
We assess the relevant channel requirements and your current data before proposing an integration. Where a supported connection exists, we scope and build it with your approval. We separate technical eligibility from commercial results and keep a useful conventional search experience in place.
Implementation & verification
We assess the relevant channel requirements and your current data before proposing an integration. Where a supported connection exists, we scope and build it with your approval. We separate technical eligibility from commercial results and keep a useful conventional search experience in place.
How we verify the work
- Confirm current platform eligibility and integration requirements.
- Compare observed discovery results without claiming control over third-party recommendations.
03Monitor discovery without inventing attribution
What you may be seeing
- We cannot tell which product information AI services can access.
- A visibility score changes, but nobody can explain what was tested.
We document product facts, access checks and a repeatable set of discovery questions. Findings show what was observed, when it was checked and what remains uncertain; inclusion in an AI answer is never promised.
Implementation & verification
We document product facts, access checks and a repeatable set of discovery questions. Findings show what was observed, when it was checked and what remains uncertain; inclusion in an AI answer is never promised.
How we verify the work
- Keep product identifiers, variants, availability and policy pages consistent across visible pages and supported feeds.
- Review crawl access and supported channel requirements for the actual platform before recommending an integration.
- Record the questions, market, date and sources behind sampled answers; treat samples as observations rather than universal rankings.
- Report identifiable referrals separately from unknown influence. Do not relabel direct or organic revenue as AI-generated without evidence.
Keep the catalogue ready for change.
We automate approved product-data updates and check differences between source records, visible pages and supported feeds. AI-assisted enrichment is reviewed against product facts before publication.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Relevance
Useful results for representative real customer queries.
Confidence
Recommendations that satisfy stated requirements.
Progress
Successful searches, trials and qualified buying actions.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Is this just for online shops?
No. Search and decision support also help customers choose software plans, technical products, services and knowledge resources. We adapt the experience to the decision.
Can AI recommend incompatible products?
It can if the system relies on generated text alone. We put compatibility, price and availability checks in application logic and use AI to explain the eligible results.
Can you guarantee visibility in AI search engines?
No. We improve the quality and consistency of your published data and supported feeds. Search platforms decide eligibility and presentation; recommendations and rankings are not guaranteed.