Custom GPTs & RAG Systems for E-commerce
Retrieval-augmented systems and custom GPTs grounded in your own documents and data — accurate, cited, and private. Engineered for e-commerce teams.
E-commerce teams don't need a generic vendor — they need custom gpts & rag systems built around how they actually operate. Retrieval-augmented systems and custom GPTs grounded in your own documents and data — accurate, cited, and private. Custom storefronts, inventory systems, and checkout optimization that lift conversion and remove operational drag.
Generic models don't know your policies, products, or history — and they hallucinate. Your knowledge is trapped in PDFs, wikis, and people's heads. For e-commerce teams specifically, your storefront is generic, inventory and orders live in separate systems, and support tickets pile up. Every point of friction costs conversion.
- 01Blueprint how e-commerce teams actually use custom gpts & rag systems today, and lock scope, price, and the metric before code.
- 02Prototype the highest-value custom gpts & rag systems loop against real e-commerce data — not a generic demo.
- 03Forge a production-grade build on Pinecone, Weaviate, pgvector, with CI/CD and observability from day one.
- 04Harden and hand off documented, ready for e-commerce teams to extend.
- Custom GPT configuration & deployment
- RAG knowledge-base architecture
- Document ingestion pipelines
- Semantic & hybrid search
- AI over your internal data
- Vector DB setup (Pinecone, Weaviate)
- Fine-tuning & prompt engineering
- Multi-source knowledge retrieval
- Custom storefronts & headless commerce
- Inventory & order management
- Checkout & conversion optimization
- WhatsApp & chat support automation
- RAG-powered product support
- Analytics & merchandising dashboards
What you can expect
Questions, answered straight
Custom storefronts, inventory systems, and checkout optimization that lift conversion and remove operational drag. We bring custom gpts & rag systems depth to those exact systems rather than retrofitting a template.