PRODUCTION RAG

From prototype to production-grade

Production RAG that actually retrieves correctly — hybrid search (vector + BM25), cross-encoder re-ranking, and iterative quality improvement. Built from experience scaling from 60% to 89% retrieval quality at 150k+ users.

4-6 weeks
9 technologies
4 deliverables
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Key Features

Hybrid search: vector + BM25 fusion

Cross-encoder re-ranking pipeline

RAGAS evaluation baseline & regression suite

Continuous learning from user feedback

How We Work Together

A proven methodology that delivers results

1

Discovery

We start with understanding your business, challenges, and goals through workshops and interviews.

2

Design

Together we design the solution architecture and create a detailed implementation plan.

3

Deliver

Iterative implementation with regular demos and feedback loops to ensure alignment.

4

Support

Post-launch support, knowledge transfer, and ongoing optimization recommendations.

Use Cases

  • Build intelligent knowledge bases
  • Question-answering over documents
  • AI-powered support agents
  • Enterprise search enhancement

Ideal For

  • Document-heavy organizations
  • Knowledge management needs
  • Customer support teams

Not Ideal For

  • No document corpus to index
  • Simple FAQ can solve the problem
  • No capacity for ongoing maintenance

Deliverables

Deliverables

  • 01
    Production-ready RAG pipeline
  • 02
    Custom vector database setup (Qdrant)
  • 03
    REST API with authentication layer
  • 04
    Monitoring and quality dashboard

Technology Stack

Python FastAPI Qdrant OpenAI LangSmith PostgreSQL RAGAS AWS Bedrock Hybrid Search

Timeline

4-6 weeks

Estimated project duration

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Ready to Transform Your Business?

Let's discuss how I can help you achieve your goals. The first consultation is free.

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