Retail & E-commerce
An AI assistant that lifted conversion
Shipped an LLM-powered shopping assistant with RAG over the product catalog and live inventory.
The challenge
Shoppers struggled to find the right product in a catalog of tens of thousands of SKUs. Search was keyword-based, and support tickets for 'which one should I buy' kept climbing.
- Keyword search failed across tens of thousands of SKUs
- Shoppers couldn't find the right product; carts stalled
- 'Which one should I buy' support tickets kept climbing
Our approach
We built a retrieval-augmented assistant grounded in the live product catalog and inventory, with evaluation and guardrails baked in from the start. It shipped behind an experiment so impact was measured, not assumed.
- Built a RAG assistant grounded in the live catalog and inventory
- Baked in evaluation and safety guardrails from day one
- Shipped behind an A/B experiment to measure real impact
The outcome
The assistant lifted conversion by 23% in the test cohort and cut pre-sales support volume, while staying accurate because retrieval stayed fresh with inventory.
- +23% conversion in the test cohort
- Lower pre-sales support volume
- Answers stay accurate as inventory changes
How it works
The path a request takes through the system.
- 1User query
- 2RAG retrieval
- 3LLM
- 4Guardrails
- 5Response
Results
- +23% conversion in the test cohort
- Lower pre-sales support volume
- Answers stay accurate as inventory changes
- Shipped behind a measured A/B experiment
What we delivered
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