ModelRefs / Matter Intake — Canonical Workflow
Matter Intake — Canonical Workflow
Matter Intake: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.
Overview
Capture prospective-client and matter facts into a restricted intake record, then route confidentiality, conflict-check, scope, engagement, and legal-advice decisions to authorized legal staff. Legal deployment requires matter- and jurisdiction-specific review of access, confidentiality, retention, privilege, and applicable professional obligations. Candidate outputs should preserve source-document and page traceability and route missing, conflicting, or interpretive issues to qualified legal reviewers. These pages describe provisional review support, not legal advice, privilege assurance, or a compliance determination.
Implementation profile
| Category | llms |
|---|---|
| Implementation maturity | enterprise |
| Primary use cases | extraction, customer-support |
| Deployment options | managed-api, hybrid |
| Architectures | serverless-api, managed-container, hybrid-private-cloud |
What this evidence does not establish
- No jurisdiction, firm policy, approved fields, conflict system, entity-resolution rules, retention schedule, staff authority, or acceptance threshold is registered.
- No measured field completeness, identity accuracy, missed-conflict indicator, sensitive-data incident, routing accuracy, or staff burden is attached.
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Sonnet 4.5
- Claude Opus 4
- Gemini 3 Pro
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
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
- GPQA Diamond — Graduate-level Google-Proof Q&A in physics, chemistry, and biology.
- MMLU-Pro — Harder MMLU successor with 10-option questions and reduced contamination.
- MMLU — Massive Multitask Language Understanding — 57 academic and professional subjects.
These are the benchmarks whose task shape bears on this workflow. Each one 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 Matter Intake — Canonical Workflow.