Turn Product Discovery into Conversation with AI Commerce

A normal chatbot answers from a fixed script or the open model and cannot see your catalog. A conversational commerce assistant is grounded in your live products, inventory, and policies, so it recommends real, in-stock items with citations and helps a purchase. The difference is grounding: one guesses, the other knows your catalog.

Not if it is built correctly, which is how we build it. Every answer is grounded in your live catalog with citations, and the assistant says so when it cannot find a match rather than inventing one. An assistant that fabricates stock or prices is worse than none, so we design against it directly.

Yes. The assistant can answer the question that caused hesitation and nudge the shopper back, while any binding commitment such as a discount is handed to a person or a controlled rule. Most abandonment is an unanswered question at the wrong moment, and answering it in context is where the recovery comes from.

With the right architecture, yes. We design inference to scale for high concurrency and cache aggressively, so the assistant stays fast when a sale hits. Peaks are exactly when a slow or failing assistant costs the most, so we load-test against your real spikes rather than assuming.

Not if it is grounded and restrained. Because it answers from real product data and hands off anything it should not decide, it helps rather than deflects, which is what shoppers notice. A helpful, accurate assistant beats a chatty but useless one, and grounding is what makes it helpful.

There is no single price; cost tracks scope. A focused build around a grounded shopping assistant is a modest, weeks-long project, while a wider rollout across your catalog, inventory, and storefront is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from higher conversion and fewer abandoned carts, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot usually reaches a working version in a few weeks, then tuning on real data, with wider rollout taking longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are guided product discovery, grounded catalog answers, comparison help, and cart recovery. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.