Product advisor
Ask useful questions and match needs to eligible products with explainable reasoning.
Conversational product guidance connected to your catalog, policies, availability, and brand rules.
A useful shopping conversation that improves discovery without inventing product facts or hiding escalation routes.
01Catalogs requiring explanation or comparison
02High-consideration purchases
03Brands with repeated pre-purchase questions
Faster product decisionsBetter comparison journeysQualified cart actions
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.
Comparison assistant
Translate catalog differences into clear trade-offs grounded in product data.
Guided bundles
Build compatible routines, kits, or configurations within availability and business rules.
Cart and handoff
Move confident decisions into cart or escalate nuanced questions to a human.
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.
- Conversation and use-case design
- Catalog and knowledge retrieval
- Product comparison and recommendation flows
Implementation and adoption
Connect the capability to real workflows, measurement, and ownership.
- Cart and handoff integration
- Safety, fallback, and escalation rules
- Quality evaluation and monitoring
Delivery process
Start narrow. Learn responsibly.
Every capability begins with the business decision, approved data, and human ownership before model or platform selection.
Study the questions
Search terms, support conversations, returns, and product complexity reveal where assistance matters.
Structure knowledge
Approved product, policy, and editorial sources become a governed retrieval layer.
Build the experience
Conversation, product cards, actions, fallback, and human handoff are implemented.
Evaluate continuously
Accuracy, usefulness, containment, and commercial behaviour are sampled and improved.
Responsible AI delivery
Useful systems need visible guardrails.
Grounded answers
Responses are constrained to approved business sources.
Visible uncertainty
The assistant does not pretend to know what the data cannot support.
Human handoff
Customers can reach a person when the request needs judgment.
Shopify Storefront APIOpenAIVector SearchGorgiasKlaviyoPostgreSQL
Questions before starting
Useful answers before the first pilot.
Can the assistant recommend products?+
Yes, using structured catalog attributes and approved rules rather than unconstrained guesses.
Can it add products to cart?+
Yes, when the experience and permissions are designed for that action.
How do you prevent hallucinations?+
We use approved retrieval sources, constrained prompts, validation, fallbacks, logging, and regular quality review.
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.