Under development

LLM orchestration, RAG and vector databases

Enterprise AI designs can add retrieval and orchestration layers before proving that approved evidence, access boundaries and operational value justify them.

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Why it matters

Architecture must separate useful grounding from unnecessary complexity, cost and data exposure.

Architecture position

Use deterministic public or enterprise evidence first; add retrieval, vector search and model orchestration only behind explicit security, quality, freshness and fallback controls.

Evidence or hypothesis

Architecture hypothesis informed by SAP agent POCs and provider-neutral design work.

Related publication

Future website-native publication

Related engineering exploration

Enterprise Intelligence Adapter

Next intended output

Publish a decision framework for when deterministic search, RAG or vector retrieval is appropriate.

Research template

Evidence, limitations and next validation.

Hypothesis: Use deterministic public or enterprise evidence first; add retrieval, vector search and model orchestration only behind explicit security, quality, freshness and fallback controls.
Method: Public architecture synthesis from approved website evidence, implementation lessons and publication metadata; not a private-client data study.
Evidence: Architecture hypothesis informed by SAP agent POCs and provider-neutral design work.
Result: Under development
Limitations: Preliminary research unless a page explicitly states otherwise; private SAP system evidence and client-specific outcomes are not published.
What it proves: Architecture hypothesis informed by SAP agent POCs and provider-neutral design work.
What it does not prove: It does not prove universal effectiveness, SAP certification, live production performance or private landscape readiness.
Next validation: Publish a decision framework for when deterministic search, RAG or vector retrieval is appropriate.