Sagent: the AI shopping assistant that turns browsing into buying

An AI shopping assistant is a conversational layer on an ecommerce site that helps a shopper decide what to buy. It reads intent from plain language, asks a few clarifying questions, and narrows a large catalog down to the handful of products that genuinely fit. Unlike site search, it doesn't need the shopper to know the product name, and unlike a support chatbot, its job is the purchase decision rather than order status.

The capabilities that matter are: understanding a need described in the shopper's own words, asking adaptive follow-up questions instead of running a fixed quiz, recommending from your live catalog, and explaining the reasoning behind each recommendation. On top of that, an enterprise-grade assistant gives you control over tone, guardrails, and merchandising rules, works across every locale you sell in, and reports back on what shoppers actually asked.

A support chatbot handles service questions: where is my order, how do I return this, what are your opening hours. An AI shopping assistant handles the decision: which of these 400 running shoes suits someone with wide feet who runs on wet trails. They draw on different data and are measured on different things. Chatbots are judged on deflection and resolution; a shopping assistant is judged on conversion, average order value, and return rate.

They do when the recommendation is good enough to be trusted. Across live Crobox deployments we've measured a 5.2x return on investment at Osprey, 16x return on investment at ASICS, and a 604% conversion rate increase at Love Stories. The mechanism is behavioral rather than technical: shoppers who understand why a product fits them hesitate less, buy more confidently, and send fewer items back.

Your product feed is the starting point, but a raw feed is rarely enough. Feeds describe products in specifications; shoppers describe them in situations and benefits. Crobox Data Enrichment translates technical attributes into that shopper language and keeps it current as your catalog turns over, which is what lets the assistant answer a question like "something warm enough for a January commute" without guessing.

With a usable product feed, a guided setup and a live preview mode get most brands to a testable assistant quickly, and our team supports each step rather than handing over a console. The work that takes real time is enrichment and tuning: agreeing which questions matter, which business rules apply, and how strongly to apply each persuasion principle. Book a demo and we'll walk through it against your own catalog.

Yes, one category and one market, live in two to four weeks depending on how fast your side can turn things around. Cost is a usage-based model, priced per conversation, so a pilot scoped to one category scales with the traffic you point at it rather than a fixed fee up front. Requirements: a product feed, a decision on the starting category, and one person who knows the catalog well enough to check the enrichment output, usually a few hours a week during setup.

Your best associates ask two or three questions, listen, then translate the answers into a recommendation. Sagent runs that same pattern at 11pm on a phone, for the shoppers who will never walk into your store. The knowledge comes from your team, since we build the model with the people who know which jacket holds up through a wet autumn.

You can point an LLM at your feed today and it will answer spec questions well. Shoppers ask in needs, something for wide feet that survives a wet winter, and a spec feed leaves the assistant guessing. Data Enrichment turns your technical attributes into that shopper language, and keeping it current as your catalog turns over is most of the build.

Sagent recommends products from your catalog, described with attributes you have approved, in your brand's tone. It has no route to inventing a product you don't sell, and when a shopper asks something outside its scope it says so and hands off. You set the boundaries before launch and review real conversations after.

Enrichment runs continuously and flags missing or incomplete attributes with automated alerts, so gaps surface instead of sitting there. The enriched data exports back into your PIM, which improves your search and merchandising at the same time. Ownership sits with ecommerce or marketing, with a light review cadence.