What's included

• A fully implemented Retrieval-Augmented Generation (RAG) pipeline with built-in observability

  The pipeline will provide immediate visibility into Query, Retrieval, and Generation stages. This includes metrics dashboards and performance monitoring tools to identify and address bottlenecks quickly. It ensures the system is optimized for real-time responses and efficient operation under varying loads.

• Integration of Pinecone or equivalent vector database

  The database will be configured with advanced optimization strategies, such as HNSW, Locality-Sensitive Hashing (LSH), and efficient indexing. Metadata segmentation will enable precise, low-latency retrievals, ensuring highly relevant results for sales inquiries and high-traffic scenarios.

• Forward-Deployed Engineering Support

  Acting as forward-deployed engineers, we will collaborate directly with stakeholders to align technical implementation with end-user workflows and business objectives. This ensures the RAG system fits seamlessly into existing operations.

• Integrated observability and feedback mechanisms

  The system will include observability tools, automated evaluations, and mechanisms for collecting feedback from the end-users. This will track key performance metrics (retrieval accuracy, response quality) and iteratively refine the system to meet evolving needs.

• CI/CD pipelines and automated deployment across development, staging, and production environments

  This includes setting up automated testing frameworks (unit, integration, and end-to-end), containerization via Docker, and logging/monitoring systems to ensure reliable performance across all environments. The infrastructure will be designed for scalability and maintainability.

• Comprehensive technical documentation for the RAG system

  Documentation will include pipeline architecture, integration instructions, monitoring/observability guidelines, and troubleshooting procedures. This will ensure your team can maintain and extend the system with ease