RAG · Retrieval · Mappings
Answers grounded in your data — not in a model's imagination
Most AI pilots fail at retrieval, not at the model. We engineer the retrieval layer first — and measure it before anything goes live.
The retrieval pipeline
One ingestion path, one query path, one measurable quality gate between them and production.
- Hybrid search & reranking — vector and keyword retrieval fused and reranked with cross-encoders: the production baseline for reliable answers.
- GraphRAG & knowledge graphs — for multi-hop questions and corpus-wide analysis that plain vector search cannot answer. We use it where it pays off, not everywhere.
- Agentic retrieval — agents that decompose complex questions, search in parallel and iterate until the evidence is sufficient.
- Grounded generation — every answer carries its sources; an honest "not in the corpus" beats a confident guess.
fig. 01 — retrieval pipelineingest · index · retrieve · ground
Data mappings & document intelligence
Retrieval is only as good as the data underneath. We make messy sources AI-ready — and structured targets reachable.
Source-to-target mappings
LLM-assisted schema mapping proposes field-level mappings between legacy systems and targets like Dynamics 365 — validated against the target schema, with low-confidence proposals routed to human review.
Visual document retrieval
OCR-free search over scanned contracts, tables and drawings with vision models — the documents where text-only RAG silently loses information.
Structured extraction
Documents become validated, schema-conformant data: strict schemas, validation-first pipelines, and human review exactly where accuracy demands it.
Which retrieval architecture?
The right architecture follows from what your knowledge actually looks like — this is one of the first whiteboard drawings in every workshop we run.
fig. 02 — retrieval architecturecyan: question · blue: route · gold: note
Scanned or visual documents — OCR-free visual retrieval searches the page image itself: tables, drawings and stamps included.
Text knowledge, factual lookups — hybrid search with reranking is the reliable production baseline.
Questions that span relationships — GraphRAG adds a knowledge graph where plain vector search stops.
Structured systems — agents query APIs and SQL directly; retrieval becomes a tool call on top of clean data mappings.
Quality is measured, not assumed
Golden test sets and LLM-as-judge gates run in CI on every retrieval, prompt or model change. You see before/after metrics — not promises.