Agentic ecommerce replaces browsing categories and filters with a conversation: a shopper describes what they need, and an AI agent finds the right products, compares them, answers questions about fit and compatibility, and completes the purchase. The outcome is higher conversion on intent-rich sessions, larger baskets from guided cross-sell, and a storefront that works for the long tail of queries a filter UI never handles.
Most conversational commerce AI pilots stall for predictable reasons. The agent is not connected to the live catalog, so it recommends out-of-stock items or quotes last month's price. Retrieval is a naive vector lookup, so it misses the exact SKU the shopper named. And there is no eval set, so nobody knows whether the agent is helping or hurting conversion. Production-grade means grounded in real inventory and pricing, retrieval as good as your best product search, and every change measured before it reaches shoppers.
What We Build
Conversational Storefronts
A chat-first shopping experience over your catalog, from discovery through checkout. Shoppers ask in their own words, the agent narrows down by asking back, shows products with real prices and availability, and hands off to your existing cart and payment flow.
Shopping Agents Grounded in Your Catalog
The agent retrieves from your live product data, inventory, and pricing, not from a snapshot in a prompt. Product attributes, variants, and bundles are indexed so the agent can compare on the dimensions that matter to your shoppers.
Retrieval on Your Existing Search Stack
We build the agent's retrieval on the OpenSearch or Elasticsearch cluster you already run, combining keyword and vector retrieval so exact model numbers and vague descriptions both work. See hybrid search explained for how the two are fused.
Guardrails and Evaluation
Hard rules that the agent may only cite products, prices, and stock levels returned by retrieval, never generated. An eval set of real shopper conversations, replayed on every change, tracking conversion, add-to-cart, and the rate of wrong or hallucinated products.
If your goal is better relevance in the search box and product listing pages rather than a conversational agent, that is covered by our ecommerce search solutions. Agentic ecommerce builds on top of that layer, and the two often ship together.
How It Runs on Your Stack
The agent runs in your cloud or on-prem, against your product data. On AWS, that is typically Amazon Bedrock for the model and Amazon OpenSearch for retrieval, with the same index serving both your search UI and the agent. Catalog changes flow through the existing indexing pipeline, so the agent sees a price update or a stock-out at the same moment the search page does. Our semantic search solutions cover the vector, hybrid, and query-understanding work in more depth.
Guardrails constrain what the agent can say and do: no products outside the retrieved set, no invented discounts, no checkout actions without explicit shopper confirmation. Every session is traced, with retrieved products, tool calls, and model outputs, so a bad recommendation can be reproduced and fixed. Latency and cost are tuned per turn.
Discoverability by third-party shopping agents matters too. Shoppers increasingly start in ChatGPT, Perplexity, or Google rather than on your site, and agentic commerce protocols are emerging that let those assistants read your catalog and complete purchases. We expose your product data in the structured form those agents expect, so one grounded catalog serves your own storefront and the external agents sending you buyers.
The same pattern, grounded retrieval plus tool calls plus escalation, is what we use for AI customer support automation, often the second agent an ecommerce team ships.
Proof
ChatMarket is a conversational storefront we launched: shoppers discover and buy products by chatting instead of browsing categories. It is the reference build for our agentic ecommerce work, with the agent grounded in the live catalog and retrieval on a hybrid search index.
From Idea to Production in Weeks
Agentic ecommerce runs through the AI Launchpad Launch Sequence:
- Use-Case Fit: pick the entry point with the clearest upside (guided discovery, gift finding, compatibility questions, reorders), audit catalog data quality, and set conversion and accuracy targets.
- Build on Your Data: catalog indexing, hybrid retrieval, agent logic, and integration with cart, inventory, and pricing, built against your real catalog from day one.
- Harden for Production: replay evals on shopper conversations, guardrails against hallucinated products and prices, latency and cost tuning, tracing.
- Launch and Run: go live on a segment of traffic, A/B against the existing flow, and iterate on the queries where the agent underperforms.
Frequently Asked Questions
What is agentic ecommerce?
Agentic ecommerce is shopping where an AI agent, rather than a category tree and filters, does the finding, comparing, and buying on the shopper's behalf. It covers both your own conversational storefront and third-party assistants that shop your catalog for their users.
How do you stop the agent from inventing products or prices?
The agent may only present products, prices, and availability that came back from retrieval against the live catalog. Output checks reject anything not in the retrieved set, and checkout actions require explicit shopper confirmation. This is tested with an eval set of real conversations before every release.
Do we need to replace our search engine?
No. The agent's retrieval runs on the OpenSearch or Elasticsearch cluster you already have, extended with vector fields for hybrid search. One index serves both your search page and the agent.
How long does it take to launch?
Under the AI Launchpad program, a conversational storefront typically reaches production traffic in weeks. The exact timeline depends on catalog complexity and how many backend integrations the agent needs.