Explain which option fits, using verified specifications and explicit compatibility rules. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
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.
01Constraint-grounded technical product-advice assistant
What you may be seeing
- Buyers cannot tell which part works with their equipment.
- Compatibility information is buried in manuals.
- Staff give inconsistent technical product advice.
- Customers return products because recommendations ignored a requirement.
- Our chatbot sounds certain even when it lacks the specification.
We help customers compare technically complex products using documented requirements, compatibility rules and evidence they can inspect.
Implementation & verification
We turn approved specifications and compatibility rules into structured product constraints with traceable source references. We code an assistant that collects missing equipment details and calls deterministic compatibility checks before recommending candidates. We use document retrieval to explain the result and its evidence while keeping unknown or conflicting specifications explicit. We validate against expert-labelled compatible and incompatible cases, with escalation for requests the rules cannot resolve.
How we verify the work
- Extract hard constraints such as model, connector, operating range and dimensions into versioned rules.
- Keep rule checking authoritative; use the language model to ask and explain.
- Require missing inputs and display the exact reason/source for each compatible option.
- Track incompatible recommendations, unresolved requests, technical return reasons and successful selections.
Tools considered for this work
Delivery: backend/rules development + retrieval + AI interface. Product data: existing PIM/ERP (a system for managing core business operations), structured SQL (the language used to query a database) tables and approved manuals. Retrieval: Elasticsearch or Qdrant; optional document extraction pipeline. Assistant: Claude tool-use API (a controlled connection between applications) or an equivalent tool-calling model. Validation: expert-labelled compatibility fixtures and conversation evaluations.
Advice with its working visible.
We connect an assistant to documented specifications and compatibility rules. It explains the evidence for a recommendation and asks for missing constraints instead of inventing a suitable product.
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.