ModelRefs / Procurement Intake — Canonical Workflow
Procurement Intake — Canonical Workflow
Procurement Intake: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.
Overview
Classify procurement requests and prepare policy, vendor, budget, risk, exception, and approval routing for authorized human review. Operations deployment requires organization-specific access, retention, approval, exception, and audit controls. Integrations with ERP or workflow systems must be validated against the target data, permissions, and failure paths. Outputs remain provisional decision support and require accountable human review before consequential actions.
Implementation profile
| Category | llms |
|---|---|
| Implementation maturity | production |
| Evidence status | partial |
| Primary use cases | extraction, enterprise-automation |
| Deployment options | managed-api, hybrid |
| Architectures | serverless-api, managed-container, hybrid-private-cloud |
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 Procurement Intake — Canonical Workflow.