Knowledge engineering
RAG & Knowledge Systems
Custom RAG development that makes trusted company knowledge searchable, permission-aware, cited, and useful inside products and workflows.
Discuss your project ↗What this solves
From promising capability to an operating product.
A useful knowledge system does more than place documents in a vector database. It connects trustworthy retrieval, permission-aware data, grounded generation, and measurable quality.
- ✓Give teams faster access to relevant information
- ✓Return evidence and citations with generated answers
- ✓Control access across sensitive knowledge sources
Capabilities
The parts required to make it work.
Data ingestion, cleaning, and document processing
Hybrid search, embeddings, re-ranking, Pinecone, pgvector, Weaviate, and Qdrant
Citation, permission, and freshness strategies
Retrieval and answer-quality evaluation
Delivery approach
A clear sequence from uncertainty to operation.
- 01
Audit
Understand the sources, formats, permissions, update cycles, and user questions.
- 02
Retrieve
Design chunking, metadata, hybrid search, and ranking around real queries.
- 03
Answer
Generate grounded responses with citations and safe fallback behavior.
- 04
Evaluate
Continuously test retrieval coverage, faithfulness, and usefulness.
FAQ
Questions about this service.
Will RAG stop every hallucination?+
No system can promise that. Strong retrieval, citations, evaluations, and fallback behavior can materially reduce unsupported answers and make them easier to detect.
Can permissions from our source systems be preserved?+
Yes. Access control should be designed into ingestion and retrieval rather than added after launch.
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