Skip to content
IDS AI Solutions
AI Pulse
Retrieval & RAGKnowledge AI

What enterprise RAG actually has to get right

Retrieval is now described as the foundation tier that grounds agents in real enterprise data. Getting it right is mostly about retrieval quality, permission-aware filtering, and knowing when to refuse.

Admin
AdminFounder & Engineering Lead · August 22, 2026 · 8 min read
What enterprise RAG actually has to get right

As agents moved into production during 2026, vendors converged on the same architectural point: the retrieval layer is what grounds an agent in an organisation’s actual data rather than the general knowledge it was trained on.

A retrieval system is judged by what it refuses to answer

The hardest engineering in enterprise RAG is not the model call. It is chunking, ranking, permission filtering, and recognising when no good source exists — and saying so instead of improvising.

The four properties that make answers actionable

  • Grounded — every answer traceable to a source document
  • Cited — the source shown, so a reader can verify rather than trust
  • Permission-aware — scoped to what the requesting user may already see
  • Evaluated — accuracy and grounding measured continuously, not assumed

Where enterprise RAG projects go wrong

Sources

  • Cognizant on the RAG foundation tier, 2026-07-28 — https://www.techtimes.com/articles/321781/20260728/cognizant-launches-emea-ai-unit-enterprise-agent-pilots-fail-scale.htm
Keep reading

Related articles

Retrieval & RAG

From RAG to GraphRAG: when vector search isn’t enough for legal, finance, and engineering docs

Vector search finds chunks similar to your query — that’s the whole mechanism. For legal contracts, financial filings, and engineering BoMs where relationships between entities matter, similarity isn’t structure. A practical guide to GraphRAG and the hybrid retrieval pattern that fits most enterprises.

May 19, 2026· 6 min read
Retrieval & RAG

Why your RAG system gets worse over time — and how to fix retrieval drift before users complain

The first 90 days, your RAG system feels accurate. By month five it’s firefighting. Four drift drivers, four detection signals, three embedding refresh strategies, and the operational practices that catch the regression in dashboards instead of customer complaints.

May 19, 2026· 7 min read
Knowledge AI

Knowledge graphs + LLMs for Vietnamese enterprises: handling language nuance at scale

Vietnamese tone marks. Compound-noun word boundaries. Company-name conventions (Công ty Cổ phần / TNHH / JSC). Administrative restructuring of districts and wards. Code-switching with English. Regional vocabulary. Six realities that break off-the-shelf retrieval — and how a knowledge-graph layer handles them.

May 19, 2026· 7 min read