E-Commerce
Conversational agents for e-commerce catalogs where choosing the wrong product is expensive. Grounded in your actual catalog, not a generic FAQ.
When the catalog is hard, the conversation is the product
Most e-commerce chatbots answer questions about shipping and returns. That is the easy half. The expensive half happens earlier, when a customer is trying to work out whether a product actually fits the thing they already own — a car, a boiler, a bike, a machine. Get that wrong and you pay for it twice: once in the return, and again in the support time spent sorting it out. A generic assistant cannot help here, because the answer does not live in a help centre article. It lives in your catalog, in fitment tables, in compatibility rules, and in the knowledge of whoever on your team currently answers those emails by hand.
Grounded in your catalog, not in a guess
ConnectAI agents are grounded in your product data. They read the same catalog your site reads, so answers reflect what you actually stock, what it fits, and what it costs today. When a customer describes their situation in their own words, the agent narrows down to the products that genuinely apply and explains why. When it is not certain, it says so and hands the conversation to a person with the full transcript attached, rather than inventing an answer that generates a return three days later.
Proven at catalog scale
PTC Auto runs ConnectAI across a catalog of more than 250,000 products in seven languages, handling customer engagement, service, and support across their website, social channels, and business phone. Catalog-grounded answers plus human-in-the-loop escalation for the edge cases produced a 40% reduction in customer service cost. That is the shape of the problem we are built for: a big, technical catalog where the pre-sale conversation decides whether the order is right.
Outcomes
- Pre-sale product questions answered from your real catalog data
- Fewer returns caused by customers ordering the wrong item
- Escalation to a human with full context when the answer isn't certain
Ideal for
- Specialist retailers with large or technical catalogs
- Stores where fit, compatibility, or specification drives returns
- Teams handling repetitive pre-sale questions by hand
Specialisms
Automotive
AI agents for automotive parts, tyres, wheels, and accessories retailers. Vehicle-aware product matching that reduces returns from incorrect fitment.
Garden & Outdoor
AI agents for garden, outdoor living, and specialist plant retailers — matching products to site conditions, dimensions, and seasonality.
Home Improvement
AI agents for home improvement and DIY retailers — matching parts and materials to the fixtures, systems, and dimensions customers already have.
See it on your own catalog
We build the agent against your real product data, so you can judge it on your own questions rather than a demo script.
Book a demo