ModelRefs / Enterprise Search — Canonical Workflow
Enterprise Search — Canonical Workflow
Canonical Enterprise Search workflow: embeddings, permissions, retrieval and grounded answer generation.
Overview
Enterprise search unifies retrieval across SaaS apps, file stores, and internal wikis behind a single grounded interface, combining embeddings, hybrid retrieval, permissions enforcement, and an LLM answerer with citations so users get one trustworthy answer instead of a dozen separate search results scattered across disconnected tools.
Use this page to check which embedding models and hybrid-private-cloud or self-hosted-cluster architectures fit your data volume and permission model, and which evidence exists for retrieval quality and citation accuracy at your expected scale, especially where source systems have inconsistent, legacy, or overlapping permission models across departments and geographies.
Workflow fit is provisional decision support, not a guarantee of retrieval completeness. Permissions enforcement, index freshness, and citation accuracy should be tested on your own corpus and access model before relying on this pattern for sensitive data, and confirm audit-logging coverage across every connected source system, including third-party SaaS connectors and shared network drives used company-wide.
Implementation profile
| Category | llms |
|---|---|
| Implementation maturity | enterprise |
| Evidence status | partial |
| Primary use cases | rag, embeddings, enterprise-automation |
| Deployment options | managed-api, self-hosted, hybrid |
| Architectures | managed-container, hybrid-private-cloud, self-hosted-cluster |
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Opus 4
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
- Text Embedding 3 Large
- Claude Sonnet 4
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Benchmarks relevant to this workflow
miracl, mkqa, mldr, swe-bench, aider-polyglot, gpqa, aime-2025, tau-bench, browsecomp-long-context, longfact-concepts, terminal-bench, mmmu, mmlu-pro, livecodebench.
Relevance is a coverage signal from the canonical registry. Each benchmark only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Enterprise Search — Canonical Workflow.