Understand the job
Capture the goal, use case and context that explain why the customer is shopping.
AI Product Discovery
Help Customers Find the Right Product Without Fighting Filters
Turn natural-language needs into a small set of relevant, explainable product choices grounded in your real catalogue and business rules.
Built around your stack
Possible connections, scoped to your systems and permissions.
ShopifyCommerce platform
PostgreSQLStructured product data
SupabaseData and access
PostHogJourney measurement
From keywords to intent.
Traditional search starts with query matching. A customer often starts with a goal: a desk for a small shared room, equipment compatible with an existing setup, or a subscription that fits a specific routine. AI discovery can translate that need into catalogue constraints without requiring the customer to learn your taxonomy.
Clarify the constraints
Ask about budget, compatibility, dimensions or other requirements that materially change the result.
Respect preferences
Use stated preferences to rank eligible products. Keep optional preferences separate from hard requirements.
See the workflow
From a natural-language need to the right fit.
Select a step to see how the context, output and controls connect.
Understand
Ask the question that changes the shortlist.
The customer describes a small desk and evening reading. Clarification turns that context into useful requirements.
Illustrative workflow
- Need
- A lamp for a small desk
- Preference
- Adjustable reading light
- Constraint
- Limited desk space
Find
Narrow the real catalogue.
Structured filters protect required dimensions and availability. Ranking helps order the remaining options.
Illustrative workflow
- Catalogue
- Desk lighting
- Required
- Compact base · available
- Shortlist
- Suitable product options
Explain
Show why each option belongs.
Use product evidence to explain the fit and the tradeoff. Keep the comparison close to the next action.
Illustrative workflow
- Fits the need
- Small base · adjustable arm
- Tradeoff
- Task light, not room lighting
- Next action
- Compare or view the product
How the journey works.
Explain the need
The customer describes a goal in their own words.
Clarify what matters
Ask a useful follow-up when the answer would change the shortlist.
Narrow the catalogue
Filter out products that fail required constraints.
Rank relevant products
Order eligible options using the agreed matching logic.
Explain the fit
Show supporting attributes and meaningful tradeoffs.
Make the next action ready
Open a PDP, compare products, add to cart or ask a specialist.
A discovery layer grounded in product data.
The architecture can combine structured product attributes with keyword or embedding search where relevant. Structured filtering protects hard requirements; business rules, inventory checks and ranking shape the shortlist. Recommendation explanations should reference the product evidence used, and analytics should show where a customer continues, revises a need or leaves. If the catalogue has no suitable match, the experience should say so and offer a useful next step.
Designed for decisions with real constraints.
These are possible applications. Each discovery experience is scoped around the actual catalogue and buying journey.
Furniture and configurable products
Match dimensions, materials and configuration requirements; make conflicting constraints visible.
Fashion, beauty and sports
Guide selection around stated preferences, use cases and approved product attributes.
Electronics and B2B catalogues
Use specifications, compatibility and availability to make technical comparisons understandable.
Subscription products
Help customers compare plan boundaries, eligibility and recurring needs using approved information.
A product experience shaped around your business.
Lumethica can design the product discovery layer around your catalogue, customer journey and business logic rather than forcing the business into a generic interaction model. Discovery can live in a guided finder, comparison interface or shopping assistant. A product sprint defines the interaction and matching assumptions before an MVP is built.
Evaluate relevance before expanding.
Start with representative customer needs and known suitable products. Review shortlist quality, incompatible results and no-match behaviour with catalogue experts. In a pilot, measure whether users engage with recommended products, use comparison and reach the next action. Query volume alone does not show whether discovery is helping customers.
Related
Explore related solutions.
FAQ
Common questions
Does AI discovery replace catalogue search?
It can complement existing search or support a specific product finder. The right interface depends on how customers express their needs and where current search fails them.
What if our catalogue has missing attributes?
First identify which attributes are essential for matching. The initial scope can use a reliable subset, with enrichment and review added for missing information.
Can customers see why a product was selected?
Yes. The interface can show relevant attributes, unmet preferences and tradeoffs. Explanations should be grounded in approved catalogue information.
Can we pilot one category?
Yes. A bounded category makes it easier to evaluate relevance, compatibility rules and customer behaviour before extending the experience.
Define the first useful release
Bring the workflow. We can help define and build the product.
Start with your catalogue, current process and the outcome you want to improve.