ai
Custom AI Model for E-commerce
Trained and fine-tuned a custom AI model on a client's catalog to power semantic search, auto-generated product descriptions, and smarter recommendations
- Client
- E-commerce Brand
- Duration
- 4 weeks
- Year
- 2026
- Category
- ai
Key Results
+38%
Search Relevance
higher click-through on search results
90%
Content Time
less time writing product descriptions
60%
Inference Cost
lower cost vs. general-purpose API calls
100%
Data Ownership
self-hosted, no data leaves infra
Technologies Used
PythonPyTorchHugging Facesentence-transformerspgvectorDocker
Challenges
- Messy, inconsistent product data unsuitable for direct training
- Capturing the brand tone of voice in generated content
- Keeping inference fast and affordable at full catalog scale
- Preventing hallucinated product attributes and specs
Solutions
- Built a pipeline to clean, label, and structure the catalog into a training set
- Fine-tuned a base LLM on curated brand content with human-in-the-loop review
- Trained a domain-specific embedding model for typo-tolerant semantic search
- Added retrieval-augmented generation to ground every output in real product data
Overview
Designed and trained a custom AI pipeline tailored to a single client's domain: fine-tuned a language model on their product catalog and support history, plus a dedicated embedding model for semantic product search. It captures customer intent and brand voice far better than any off-the-shelf model, and runs on the client's own infrastructure for full data ownership.