RAG Development & Implementation
We build retrieval-augmented generation systems that connect LLMs to your data with precision, evaluation, and production-grade reliability.
- Vector search, hybrid retrieval, and chunking strategies
- Evaluation pipelines for relevance, accuracy, and hallucination
- Enterprise data connectors and access control
What we do
We design and build RAG systems that go beyond basic vector search. Our approach includes chunking strategy, embedding selection, hybrid retrieval, re-ranking, and end-to-end evaluation — so your LLM answers are accurate, grounded, and auditable.
Use cases
Representative ways teams deploy this capability in production.
Knowledge base Q&A
Problem: Employees need fast, accurate answers from internal docs.
Solution: RAG pipeline over approved sources with citations and access control.
Result: Faster answers, fewer hallucinations, auditable responses.
Customer support with context
Problem: Support agents need product and account context in real time.
Solution: RAG-powered assistant that retrieves relevant docs per query.
Result: Lower handle time, consistent quality, reduced escalation.
Legal document search
Problem: Lawyers need to find clauses and precedents across thousands of documents.
Solution: Semantic search with metadata filtering and citation extraction.
Result: Hours of review reduced to minutes with traceable sources.
Technical documentation assistant
Problem: Engineers waste time searching across wikis, repos, and runbooks.
Solution: RAG system with multi-source retrieval and code-aware chunking.
Result: Faster onboarding and fewer repeated questions.
How it works
- Data audit & chunking strategy — Analyze sources, formats, update frequency, and access patterns.
- Embedding & retrieval design — Select embedding models, vector DB, hybrid search, and re-ranking.
- Pipeline development — Build ingestion, chunking, indexing, and retrieval pipelines.
- Evaluation & testing — Measure relevance, accuracy, latency, and hallucination rates.
- Deployment & monitoring — Production rollout with logging, drift detection, and alerting.
Architecture & technology
Our RAG architectures include embedding pipelines, vector databases, hybrid retrieval (dense + sparse), re-ranking, and evaluation frameworks — designed for accuracy, latency, and cost control at scale.
Why work with us
- Deep experience with production RAG systems
- Evaluation-first approach (relevance, accuracy, hallucination)
- Support for complex data: PDFs, code, structured + unstructured
- Integration with existing auth and access control
Let's discuss your project
Technical conversation first. We'll map the shortest path from your goal to a reliable production system.
Related Services
FAQ
What vector database do you recommend?
It depends on scale, latency, and hosting requirements. We work with Pinecone, Weaviate, Qdrant, pgvector, and others.
How do you handle document updates?
We build incremental ingestion pipelines with change detection and re-indexing.
RAG vs fine-tuning — which is better?
RAG is better for dynamic data and citation needs. Fine-tuning is better for style and domain adaptation. We often combine both.
How do you measure RAG quality?
We use automated evaluation: relevance, faithfulness, answer correctness, and hallucination detection.
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