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Where RAG Systems Are Deployed

RAG (Retrieval-Augmented Generation) has become the go-to architecture for enterprise AI. Instead of relying solely on an LLM's training data, RAG systems retrieve relevant documents from your own data and use them to generate accurate, grounded responses.
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Data as of Nov 10, 2026
Dataset details
Revision
dsr-fc53bf0883afdfe4
Canonical records
3,963

Executive brief

RAG Systems is a top-2 mover among 9 peers on 6-month momentum.

Show metrics

Cases

355

74 in the last 6 months

Momentum

56Rising
#2 of 9Peer momentum

Innovativeness

3.5Advanced

100% of evidence scored

Cases trend

Cases 1Agent 1
How this executive brief is measured

Concentration compares this view's share of source-linked deployments for a use-case type with that type's share across the full catalog. Momentum is a peer-relative 0-100 score based on recent deployment volume, acceleration, recent evidence share, and evidence depth. Quantified outcome medians appear only when at least 4 reported metrics support them; smaller supported samples are marked early evidence.

Implementation

Do teams build, buy, or compose this?

How the documented deployments in this view were built — custom engineering (Build), an off-the-shelf assistant (Buy), or low-code assembly (Compose).

342 classified cases
BuildBuyComposeMixed

342 of 355 cases classified (96%) · Compare all use-case types

Full report

Expand any section for the detail behind the summary above.

Reported challenge examples: Accelerate development of AI-driven business solutions (2 cases), Business users needed a way to query complex datasets without SQL expertise (2 cases), Complexity in integrating diverse enterprise data for support agents (2 cases), Concerns around enterprise data security, privacy, and regulatory compliance (2 cases), and Enterprises require secure, governed AI adoption in production business systems (2 cases). Evidence is still limited; expand to inspect the source cases.

Adoption pulse: 74 of the 355 cases in this view were published in the last 6 months. Expand for the adoption curve.

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