RAG & Knowledge-Aware Systems

Ground LLM responses in your proprietary data and knowledge bases for accurate, trustworthy outputs.

How RAG Works

RAG retrieves relevant context from your knowledge base at query time, so the LLM answers from your data — not from general training.

Ingestion Pipeline (runs once / on update)
Documents / Data→Chunking→Embedding Model→Vector Database
Query Pipeline (runs on every user question)
User Query→Embed Query→Retrieve Top-K→Re-rank→Augment Prompt→LLM→Grounded Answer

What we build

Retrieval-Augmented Generation (RAG) is the most reliable way to make LLMs useful with your proprietary data — without the cost and risk of full fine-tuning. We design RAG architectures that retrieve the right context at query time, so your AI systems answer from your knowledge, not just from general training data.

Our RAG systems are built for production: optimized retrieval pipelines, robust chunking strategies, re-ranking for relevance, and monitoring for answer quality and retrieval accuracy.

Key deliverables

  • Vector database selection, design, and indexing (Pinecone, Weaviate, pgvector, and others)
  • Document ingestion pipelines with chunking and metadata strategies
  • Hybrid retrieval combining dense and sparse search
  • Re-ranking and relevance scoring for retrieval quality
  • Query routing and multi-index retrieval architectures
  • RAG evaluation and answer quality monitoring
90%
Reduction in hallucination vs base LLM
10×
Cheaper than full fine-tuning for knowledge tasks
<200ms
Retrieval latency with optimized vector search
100M+
Documents indexable in production RAG systems

Real-Life Use Cases

RAG systems delivering accurate, grounded AI responses across industries.

Publishing

Technical Documentation Q&A

A software company built a RAG system over 50,000 pages of technical documentation. Developers ask questions in natural language and get precise, cited answers. Support ticket volume dropped 42% as developers self-serve. Answer accuracy is 94% vs 61% for the base LLM.

42% fewer support tickets, 94% answer accuracy
Legal

Case Law Research Assistant

A law firm indexed 20 years of case law, briefs, and legal memos into a RAG system. Associates now research precedents in minutes instead of hours. The system cites specific documents and page numbers. Research time per matter dropped 65%.

65% reduction in legal research time
Manufacturing

Equipment Maintenance Knowledge Base

A manufacturer built a RAG system over maintenance manuals, repair logs, and engineering specs for 2,000 equipment types. Technicians get instant, accurate repair guidance on-site. First-time fix rate improved from 67% to 89%.

First-time fix rate: 67% → 89%
Banking

Regulatory Compliance Assistant

A bank built a RAG system over their regulatory library — 15,000 pages of policies, regulations, and guidance. Compliance officers get instant answers with citations. Time to answer compliance queries dropped from 2 days to 15 minutes.

Compliance query time: 2 days → 15 minutes

Make your knowledge base AI-searchable

We'll design and build the RAG system that grounds your AI in your proprietary data — accurately and at production scale.

Build Your Knowledge System