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RAG & Retrieval Systems — Trusted AI Answers from Your Data
Retrieval is the foundation of every reliable AI system. If the model cannot find the right source, the answer is wrong no matter how eloquently it is written. We design and build retrieval systems that connect language models to your documents, databases, and knowledge bases with measurable quality, clear citations, and graceful failure handling.
Start a retrieval systems projectRetrieval architecture designed for accuracy
- Hybrid search combining semantic embeddings and keyword matching
- Intelligent chunking strategies that preserve context across splits
- Re-ranking models that surface the most relevant passages first
- Source filtering by date, authorship, department, and access level
Evaluation pipelines you can trust
- Benchmark retrieval quality before any change goes to production
- Measure recall, precision, MRR, and citation coverage on real questions
- Track drift as your knowledge base grows and evolves
- Automated regression tests that catch degradation before users do
Production-ready infrastructure
- Vector stores with automatic reindexing and backup strategies
- Metadata pipelines that keep source freshness and ownership current
- Monitoring dashboards for query latency, error rates, and cost
- Scalable architecture that grows from thousands to millions of documents
Ready to build your retrieval systems?
We design and ship production AI systems with the controls, evaluation, and monitoring your team needs.
Start a project