How We Cut Support Tickets by 33% with a RAG-Powered Product Search

The problem

FreshCart, a fast-growing online grocery platform, was drowning in a specific kind of support ticket: "Do you have gluten-free oat milk?" "Is this detergent safe for babies?" "What's a substitute for tamarind paste?" Their keyword-based site search couldn't answer any of it, so customers gave up and messaged support instead. Support headcount was scaling faster than revenue.

Why keyword search wasn't enough

Traditional search matches strings. It has no concept of "similar to" or "safe for" or "substitute for." Every one of those queries needed the system to actually understand the product catalog — ingredients, allergens, use cases — not just match text against product titles.

What we built

We designed a retrieval-augmented generation pipeline that sits between the customer and the product catalog:

• Embedding layer: every product description, ingredient list, and review was chunked and embedded into a vector store, refreshed on every catalog update.
• Retrieval step: customer queries are embedded and matched against the closest catalog entries, pulling in structured metadata (allergens, dietary tags, stock status) alongside the raw text.
• Generation step: an LLM turns the retrieved context into a direct, conversational answer — recommending an actual in-stock substitute rather than a generic response.
• Guardrails: the system only answers from retrieved catalog data. If nothing relevant is found, it hands off to a human agent instead of guessing.

The result

Within a month of launch, product-related support tickets dropped by roughly a third. Customers were getting answers in the search bar instead of a support queue, and the tickets that did reach human agents were higher-value — the kind that actually needed a person.

Why this matters beyond grocery

The pattern here applies to any business sitting on a large, structured catalog — SaaS documentation, real estate listings, financial products, medical formularies. If your support team keeps answering the same category of question, there's a good chance RAG can answer it at the point of search instead.

If your product search or support flow feels like this, get in touch — we can usually tell within a short call whether RAG is the right fit before you commit to a build.