Most enterprise RAG demos work on a clean corpus and fall over the moment they meet real data, real questions, and real users. We build retrieval systems engineered for grounding — hybrid retrieval, reranking, context engineering, and evaluation that holds up against your actual workflows.
Enterprise intent for hybrid retrieval tripled in a single quarter of 2026 — a signal that the original retrieve-then-generate pipeline is failing at scale. Production RAG isn’t a vector DB; it’s an engineered context system, and most teams are missing the engineering.
Top-k embedding search misses the question entirely on enterprise corpora. Without hybrid retrieval, BM25 fallback, and metadata filters, you get plausible-sounding but ungrounded responses — the demo passes, the audit doesn’t.
Stuffing the window with irrelevant chunks degrades answer quality. Without a reranker and a context budget, the model drowns in noise — and the operator stops trusting the citations.
Faithfulness, context precision, context recall — most teams run none of them. You can’t improve what you can’t measure, and “looks right” isn’t a deployment criterion.
Documents change, access changes, classification changes — and the index doesn’t. Without lifecycle management and permission-aware retrieval, you have a data leak waiting to happen.
Retrieval is one piece. Production-grade systems engineer everything between the question and the answer.
Three production retrieval patterns — each engineered for a different shape of question and a different shape of corpus.
Grounded Q&A over policies, manuals, contracts, tickets, and internal documentation. Citations and audit trail by default. Permission-aware from the first query.
Multi-hop, tool-using retrieval for complex questions that need decomposition, lookup, synthesis, and verification. The retriever becomes an agent, not a single shot.
Knowledge-graph-enhanced retrieval for domains where entities and relationships matter — regulated finance, pharma, complex sales, legal. Measured above 81% accuracy in specialized domains.
The retriever is one piece. These are the layers that make it trustworthy in production.
Vector plus lexical plus structured, with metadata filters and access controls. The index becomes a first-class system, not a side car.
Faithfulness, grounding, context precision, drift — with regression testing every release. RAGAS, custom harnesses, or both, grounded in your real questions and real failure modes.
Index refresh, permission propagation, citation integrity, PII protection. The retrieval system stays current with the source of truth — and inside your access policies.
The strongest 2026 use cases share a shape: high-stakes questions over large, messy, access-controlled corpora — where a wrong answer has a cost and a citation is the difference.
Grounded answers over product docs, ticket history, and knowledge bases. 89% of RAG deployments target this; production systems report 70–90% hallucination reduction.
Across SharePoint, Confluence, Slack, ticketing, and HRIS. Permission-aware retrieval as a first-class concern, not an integration afterthought.
68% of legal departments now use RAG tools. Citations and audit trail are non-negotiable; we engineer for them from the first index.
Filings, reports, calls, internal models. Reasoning over evidence with full provenance — the analyst keeps their accountability.
Pharma, financial services, healthcare. Grounding directly against the controlling document, with citation to the clause that drove the answer.
Manuals, prior tickets, parts databases. Retrieval on the edge or in the field, offline-tolerant where the network won’t hold.
Yes — but "RAG" now means something different. The 2024 retrieve-then-stuff pipeline is dead. Production RAG in 2026 is hybrid retrieval, multi-stage reranking, context engineering, and continuous evaluation. The pattern hasn’t been replaced; it’s been engineered.
Rarely — but decisively when it fits. GraphRAG earns its place when entities and relationships drive the answer: regulated finance, life sciences, complex B2B sales, legal. On most enterprise corpora, hybrid retrieval plus a good reranker beats a graph at a fraction of the build cost. We test before we recommend.
Faithfulness, answer relevance, context precision, context recall — measured continuously, not at launch. RAGAS or a custom harness, grounded in your real questions and your real failure modes. If you can’t show a regression dashboard on every release, the system isn’t production.
Per-document, per-query, at index time and at retrieval time. Permission propagation from the source system into the index, with filters applied before reranking — never as a post-hoc redaction. If the user can’t see the document in SharePoint, the retriever should never have seen it either.
Yes. The code, the prompts, the chunking strategy, the eval harness, the integrations — all yours. We design every engagement so your team can operate, extend, and reindex independently. Enablement is built into the project, not sold as an upsell.
A working production system in 8–14 weeks for a defined corpus and use case. The two-week Knowledge System Review tells you the realistic scope for your specific data, access model, and accuracy bar — before you commit to a build.
We audit it against the five production criteria most teams skip — hybrid retrieval, multi-stage reranking, an evaluation harness, governance and permissions, and grounding integrity. You leave with a scored readiness assessment, a target architecture for your environment, and a staged delivery plan with effort estimates.
What you get: a production-readiness assessment scored against twelve criteria; a target retrieval architecture for your corpus, access model, and latency budget; a staged delivery plan with timelines and effort estimates; and one workshop with your engineering and data leads. Led by a senior consultant — fixed scope, fixed fee.
Book a Knowledge System Review →A 30-minute conversation with a senior consultant. Bring a RAG system that’s stuck in pilot, or a corpus you’ve been told is “too messy to use.” We’ll tell you whether it’s ready to ship, where the gaps are, and what a Knowledge System Review would surface.
Book a Knowledge System Review →