When it fits.
Choose RAG when answers depend on private or changing information: internal policies, support documentation, contracts, or product knowledge. Retrieval brings relevant source material into a model’s context without retraining the model whenever a document changes.
What we build together.
- Document ingestion with parsing, chunking, metadata extraction, and update handling.
- Permission-aware retrieval, hybrid search (dense + BM25), and reranking where evaluation supports them.
- An answer interface with source citations and a clear response when evidence is missing.
- A representative evaluation set, retrieval diagnostics, and production monitoring.
Architecture & trade-offs.
| Capability | RAG (Retrieval-Augmented) | Traditional Keyword Search | Model Fine-Tuning |
|---|---|---|---|
| Private Knowledge Access | Real-time retrieval from live documents | Keyword matching without semantic understanding | Static weights; retrain needed for new data |
| Hallucination Control | High (answers backed by cited source chunks) | N/A (returns link or raw excerpt) | Moderate (model may hallucinate plausibly) |
| Data Freshness | Instant (updates on document re-indexing) | Instant (search index updates) | Requires new dataset and fine-tuning run |
| Access Control / Permissions | Enforced per user at retrieval time | Enforced via ACL filters | Cannot isolate permissions within model weights |
Start with the right questions.
We start with representative documents and real user questions. We examine access rules, update frequency, language coverage, and failure costs. An evaluation baseline lets us compare retrieval strategies before choosing a vector database or adding complexity.
Engineering for the real world.
RAG does not guarantee correct answers. Missing evidence, conflicting documents, and retrieval failures need explicit handling. We evaluate both whether the correct source was found and whether the answer faithfully represents it.
Questions before we begin.
How is RAG different from fine-tuning?
RAG supplies knowledge at answer time from external documents. Fine-tuning changes model behavior and style through training examples. They solve different problems and can be combined when evaluation shows a need.
Can a RAG system respect document permissions?
Access rules should be enforced during retrieval so a user cannot receive content from sources they cannot access. We scope identity, permissions, and indexing boundaries before implementation.
What do you need to estimate a RAG project?
Representative sources, expected questions, user roles, traffic expectations, integration requirements, and security constraints. Pricing and delivery dates are agreed after scope review.
Explore the thinking. See the work.
Read our engineering guide to production RAG
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What are you looking to solve?
Tell us about the workflow, users, and constraints. We’ll define a practical next step together.
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