Semantic retrieval
Embeddings and vector search locate conceptually related passages even when the wording differs.
AI / Search, RAG & Hybrid Retrieval
Search by meaning. Constrain by architecture.
Nornoet designs retrieval systems that combine RAG, semantic search, vector databases, keyword search, structured queries, metadata, permissions, deterministic rules, citations, and human escalation.
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Hybrid by Design
Semantic where meaning matters. Deterministic where rules matter.
Documents often benefit from semantic and keyword retrieval. Structured facts may require SQL, APIs, or deterministic query layers. Metadata and permissions constrain both. The best system blends the methods instead of forcing every question through one fashionable component.
Retrieval Architecture
The right method for each kind of information.
Keyword and full-text search
Exact terms, names, clauses, identifiers, and phrases remain available when precision matters.
Structured retrieval
SQL, APIs, reporting models, and deterministic query layers retrieve facts from tables and operational systems.
Metadata and repository scope
Document types, entities, dates, statuses, folders, sources, and lifecycle state narrow the search correctly.
Permission-aware access
The retrieval layer preserves the access boundaries and source permissions the organization already relies on.
Ranking and validation
Reranking, source authority, version rules, citations, confidence requirements, and escalation reduce plausible but unsupported answers.
Retrieval Flow
Find evidence before generating an answer.
- 01
Understand the request
Identify the user intent, relevant business entities, requested information type, and ambiguity that must be resolved.
- 02
Constrain the search
Apply identity, permissions, authoritative sources, repositories, document types, metadata, dates, and version rules.
- 03
Route by information type
Use deterministic queries for structured facts and semantic, keyword, or repository-native search for documents and knowledge.
- 04
Retrieve and rerank
Combine eligible results, compare authority and relevance, and select the evidence appropriate to the request.
- 05
Validate and cite
Check sources, versions, calculations, contradictions, completeness, and the evidence supporting the response.
- 06
Answer, clarify, or escalate
Return a supported result, ask for missing context, or stop when the architecture cannot establish a trustworthy answer.
RAG and Vector Databases
A component, not the architecture.
Nornoet can build ingestion pipelines, document parsing, OCR, chunking, embeddings, vector indexes, hybrid search, reranking, incremental updates, deletion propagation, and retrieval evaluation. We use them when they improve the system, not because every repository needs a vector database attached to it.
Operational Accuracy
Authority, versions, and permissions travel with the content.
When a document is restricted, superseded, moved, or deleted, the retrieval architecture must update with it. Production-ready retrieval includes permission synchronization, version control, source provenance, citations, monitoring, and tests built from real organizational questions.
Assessment