Hybrid retrieval
Combine exact-match, semantic, behavioral, and merchandising signals.
AI Search & Product Discovery
Discuss this capabilitySemantic, conversational, and multimodal discovery for catalogs where conventional search leaves customers stranded.
More relevant discovery for natural-language, incomplete, misspelled, or visually-led product searches.
01Large or technically complex catalogs
02Stores with weak zero-result and refinement journeys
03Customers who describe needs rather than product names
Fewer dead-end searchesNatural-language discoveryMeasurable relevance gains
Capability modules
Choose the modules the use case needs.
The service is assembled around the decision, journey, and operating model—not sold as one oversized platform.
Conversational refinement
Use follow-up questions to narrow broad, ambiguous, or technical needs.
Visual discovery
Support image-led similarity and attribute discovery where the catalog warrants it.
Relevance laboratory
Maintain judged queries, zero-result review, experiments, and merchandising controls.
Engagement scope
What responsible AI delivery can include.
AI is only one component. Data, workflow design, interfaces, integration, governance, evaluation, and adoption determine whether it becomes useful.
Foundation and use case
Define the decision, approved information, and operating boundaries.
- Search and zero-result audit
- Catalog attribute and taxonomy work
- Semantic retrieval implementation
Implementation and adoption
Connect the capability to real workflows, measurement, and ownership.
- Ranking and merchandising controls
- Conversational or visual search interface
- Relevance evaluation dashboard
Delivery process
Start narrow. Learn responsibly.
Every capability begins with the business decision, approved data, and human ownership before model or platform selection.
Analyze demand
Queries, refinements, exits, and catalog structure expose relevance problems.
Prepare the catalog
Attributes, synonyms, descriptions, and embeddings are improved.
Implement retrieval
Semantic candidates, business rules, ranking, and interface behaviour are connected.
Measure relevance
Judged query sets and behavioural signals guide iteration.
Responsible AI delivery
Useful systems need visible guardrails.
Merchandising control
Commercial rules remain visible and editable.
Explainable evaluation
A representative query set is reviewed before rollout.
Graceful fallback
Conventional search and navigation remain available when they are stronger.
ShopifyAlgoliaTypesenseOpenAI EmbeddingsPostgreSQLGA4
Questions before starting
Useful answers before the first pilot.
Is semantic search always better?+
No. Exact SKU, brand, and navigational queries may still favour conventional retrieval. Strong systems combine methods.
Do we need to rewrite product data?+
Often some enrichment is needed. Search quality cannot exceed the usable product information available.
Can merchandisers control results?+
Yes. Boosting, pinning, exclusions, campaigns, and business rules remain part of the system.
Let’s work together
Start with one
useful pilot.
Tell us the workflow, customer problem, or repeated decision. We will help judge whether AI is appropriate and what a responsible first version should prove.