GenAI RAG Application Development

Objective

Ground an LLM agent in real-world data using the retrieval-augmented generation (RAG) architecture pattern.

Description

RAG applications come in many forms, from citation-enhanced chatbots to complex research assistants. They can be productivity tools for knowledge workers, business intelligence tools for decision makers, or product offerings for customers.

Building on a strong foundation of ML application development, we've mastered the art of applying new LLM technology to knowledge-intensive applications. We are among the few who have gone beyond proof of concept technical demos, and learned the hard-won lessons that come from deploying these systems at scale. As we've learned, GenAI products become truly valuable when they reach a threshold of reliability. Building a reliable application out of unreliable components is our specialty.

Engagement Details

Key Activities

• Discovery: We'll work with you to assess data sources, define the application requirements, and align on solution architecture and design.
• Implementation: We'll rapidly develop and deploy an application, and continue to iteratively improve based on performance and feedback.
• Enablement: With comprehensive documentation and operational guides, we'll hand over the system ownership to your team, enabling them to operate and extend it.

Deliverables

• Full stack GenAI application and source code
• Performance evaluation system for confidently estimating the reliability of the system
• Operational documentation and handover

Highlights

• Cloud agnostic
• LLM provider agnostic