A support chatbot answers questions about orders and returns. An AI shopping assistant sells: it guides a shopper from a vague need ("I'm redoing my kitchen", "I need a TV for a bright room") to the right products, asking the questions a good salesperson would ask about budget, constraints, and context. Kleio's assistants are built on a Knowledge Engine that maps the full catalog, product attributes, and per-store availability, so recommendations are accurate, current, and purchasable, ending in a cart, an appointment, or a store visit rather than a deflected ticket.

Yes, and it can be measured rather than claimed. Across Kleio's enterprise deployments, a 1,250-agency European real estate network measured +7.3% qualified leads from visitors with the assistant available, counted across every conversion channel. The mechanism transfers directly to retail: most visitors leave without converting not because the offer is wrong, but because choosing from a large catalog is hard. A digital sales advisor closes that gap, and every point of conversion gained compounds against traffic you have already paid for.

Kleio's enterprise deployments today are concentrated in automotive retail, real estate, and travel, including Emil Frey (Autosphere), Orpi, and TTC Tour Brands. These verticals share retail's defining characteristics: considered purchases, large and complex catalogs, and distribution through store or agency networks. Retail deployments are in development, and the underlying Knowledge Engine architecture transfers directly. Reference conversations with adjacent-vertical clients are available on request.

For most retail networks, the website's job is not the checkout, it is the appointment or the store visit. Kleio agents qualify the shopper's project in conversation, then convert it into the action your stores can close: a design appointment for a kitchen network, an eye test for an optics chain, a click-and-collect order backed by live per-store stock. Every handover carries the full context of the conversation, so store teams receive a qualified customer, not an anonymous form submission.

Complex catalogs are the reason Kleio exists. The Knowledge Engine is a five-store architecture built to unify product data, pricing and availability, customer context, relationships between products, and documentation, precisely because enterprise catalogs are large, inconsistent, and spread across systems. That is what lets an agent reason over tens of thousands of SKUs, resolve compatibility ("does this fit?"), and recommend coherent selections rather than keyword matches. Generic chatbots fail on exactly this point.

Comparable deployments in catalog-heavy verticals run 8 to 12 weeks. Most of that time goes into modelling the catalog and its attributes, connecting product data, stock, and store systems, indexing documentation so agents answer accurately, and configuring the conversion actions, appointment booking, cart building, or store routing. Customer-facing assistants and in-store advisor copilots deploy in parallel, and a randomised measurement protocol can be instrumented from day one so impact is proven on your own traffic.

Yes. Kleio connects to commerce platforms, PIM and product data systems, per-store inventory, appointment and booking tools, and CRMs including Salesforce, Microsoft Dynamics, and HubSpot through standard connectors and APIs. The Knowledge Engine ingests catalog, pricing, and availability data in real time, so agents never recommend an out-of-stock product, and every conversation, appointment, and cart syncs back to the systems your teams already use, with the shopper's context attached.

Yes. For specialist retailers, expert advice is the brand, and it is also the hardest thing to scale beyond store hours and store walls. Kleio replicates that advice online: a hi-fi specialist's system-matching, a baby retailer's safety-first guidance, a jeweller's gift consultation, available to every visitor, at any hour, in any language. The same platform serves large store networks and specialist e-commerce; what changes is the catalog it reasons over and the conversion it drives.