# Conversational product search for a global sportswear e-commerce store
> A global sportswear brand wanted to know whether shoppers would use natural language to find products, without rebuilding its highest-converting pages. In under six weeks, three engineers and a designer built a conversational layer on top of the existing product page, trained on 500-plus products, and tested it with real shoppers. Shoppers explored more products and reported more confidence, and the brand added the concept to its roadmap.

Client: Global sportswear brand · Industry: Retail & consumer · Practices: AI & Agents · Timeline: Under 6 weeks
## Key results
- **6 weeks** to a deployed, user-tested prototype
- **500+** products in the trained catalogue
- **4** people on the build — three engineers, one designer
## The challenge

**Shoppers describe. Dropdowns cannot listen.**

Twistag built a conversational product discovery layer for a global sportswear brand — natural-language search embedded directly in the existing product detail page, trained on a catalogue of 500-plus products, deployed and user-tested with real shoppers in under six weeks by three engineers and a designer. The brand's shoppers could describe what they wanted ("something bold and minimal for a city weekend") but could only express it through a colour dropdown. Twistag built a headless component that layers over the existing PDP without touching the e-commerce platform, using Pinecone for semantic product matching at sub-500ms latency and LangGraph for conversation state. The brand is not named publicly.

Product detail pages have looked the same since the early 2010s — hero image, tabs, size selector, filters in a sidebar — while the way people interact with software has changed completely. Shoppers ask questions. They describe what they want in natural language and expect the system to understand intent. A shopper looking for "something bold and minimal for a city weekend" had no way to express that through a colour dropdown. The commercial constraint made experimenting hard: product pages are the highest-converting pages on any e-commerce site, which makes them the riskiest place to change anything. The brand's digital team did not want a PDP rebuild — they wanted to know whether conversational commerce could be validated quickly and cheaply, with enough realism to produce behavioural signal from real shoppers.

## The approach

**A layer on the page, nothing rebuilt.**

1. **On top of the page.** The conversation is an optional layer over the existing product page, so shoppers who prefer browsing are unaffected and the e-commerce platform is untouched. 
2. **Search by meaning.** Pinecone stores every product as a vector and returns the closest matches to a description in under 500 ms, while LangGraph keeps track of the conversation for follow-ups and comparisons. 
3. **A knowledge layer for fashion.** Shopper language is translated into product attributes: “casual but not boring” maps to style characteristics, “gym to dinner” narrows differently from “weekend travel”, and the tone matches the brand. 
4. **Six weeks, weekly demos.** Discovery in week one, build with weekly demos in weeks two to five, and testing with real shoppers in week six. 

## The impact

**Validated with real shoppers in six weeks.**

Twistag delivered a functional prototype in under six weeks with three engineers and one designer. User testing with real shoppers confirmed the hypothesis: shoppers engaged more deeply, explored more products, and reported higher confidence in their selections. The brand advanced the concept to its production roadmap. Because the component is headless, a production rollout can proceed page by page or per A/B segment without a PDP rebuild. These are process and qualitative outcomes, stated as such — this case study reports no conversion, revenue or retention figure, because none has been published by the brand.

> The engineering value lies in the knowledge layer that translates between user intent and the brand's product reality.

## Technologies used

- Pinecone
- LangGraph
- Next.js
- Node.js

> Twistag built a conversational product discovery layer over a global sportswear brand's existing product detail page, trained on 500-plus products.

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Source: https://twistag.com/case-studies/conversational-ecommerce-ai-product-discovery