Retail & E-Commerce AI Engineering

It varies widely by catalog and traffic, so we do not promise a figure up front. We measure lift against your current experience with a proper holdout, because a recommendation engine that looks good in a demo can underperform your existing merchandising. Honest lift on your data is the only number worth quoting.

For new users and products with no history, we fall back on content and attribute signals until behavior accumulates, rather than showing nothing or only bestsellers. Cold-start is a design decision, not an afterthought, and how gracefully the engine handles it is often what separates a useful one from a frustrating one.

Usually because search matches keywords literally and misses intent, synonyms, or exact codes. We combine meaning-based and keyword search so a shopper who types a description or a part number both land on the right product. Empty results on in-stock items are lost sales, and they are fixable.

Yes, with the right architecture. We design inference to scale for peak concurrency and cache aggressively, so recommendations and search stay fast when traffic spikes. Peak events are exactly when a slow or failing model costs the most, so we load-test against your expected spikes rather than assuming.

You do. Your catalog, order, and behavior data, the trained models, and the code remain yours, with privacy-respecting handling of shopper data. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single product recommendation build is a modest, weeks-long project, while a wider rollout across your catalog, order, and behavior systems 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, fewer stockouts, and less fraud loss, 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 on one use case such as product recommendation usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes 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 personalization and recommendations, demand forecasting, AI catalog search, and fraud detection. 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.