Use case
Contract review
Reading an agreement against a checklist: which clauses are present, how they deviate from the standard position, what the renewal and liability terms say. Output is a marked-up list of issues for a lawyer, not an opinion on whether to sign.
- Source
- Editorial ontology entry — no fetched document behind this page
- Evidence
- none on this page — it links to the pages that hold it
- Category
- Documents
- Typical risk
- high
- Entry
- editorial · reviewed 25 Aug 2026
01What this is
Contract review extracts clauses and compares them to a playbook — the positions a firm or company will accept. It works over incoming third-party paper and over an existing contract estate, where the question is usually "which of our 4,000 agreements contain this term".
A good deployment cites the clause text it relied on, shows deviation against the named playbook position, records who accepted each finding, and keeps a full audit trail. Recall matters more than precision: a missed clause is invisible, a false positive is merely dismissed. Accuracy is measured on a labelled sample before rollout.
Pitfalls: playbooks that exist only in a partner's head; scanned PDFs whose text was never extracted properly; and confident summaries of amended agreements where the operative term sits in a side letter. Client contracts are confidential and frequently privileged, so vendor training and retention terms decide whether a SaaS route is available at all.
- Typical data
- contracts, client documents, legal documents, scanned documents
- Solution classes it admits
- Enterprise SaaS, Private cloud, Self-hosted
02Deployment options
- ASSESSMENT
On a neutral reading of this use case, Self-hosted is a strong alternative, Private cloud is a strong alternative and Enterprise SaaS is conditional.
- ASSESSMENT
Read as a generic reading of this page, not a recommendation: no organisation, size, jurisdiction, budget or technical capability has been supplied, so wherever an option depends on one of those, it says "unknown". Ask your own question to get a verdict that accounts for them.
Private cloud
STRONG ALTERNATIVEManaged model API or private model deployment in your cloud account
- ASSESSMENT
Two patterns fit inside one account in a region you name: call a managed foundation-model API such as Amazon Bedrock, Microsoft Foundry / Azure OpenAI, or Vertex AI; or deploy an open-weight model on GPU compute you control. Your application, retrieval layer, storage, identity and logs remain in your cloud boundary in both patterns.
- ASSESSMENT
The managed-API pattern can use closed-source frontier models without buying or operating GPUs. It is usually the fastest way to build a custom workflow, but prompts and retrieved context are processed by the managed service, so model availability, retention, abuse monitoring and regional routing must be checked for the exact feature and endpoint.
- ASSESSMENT
The private-model pattern gives more control over weights, serving and network paths, and can use managed endpoints or your own containers. It also makes your team responsible for capacity, patches, model upgrades, evaluation and failover.
- ASSESSMENT
The cloud provider becomes a data processor in either pattern: you need a DPA, a documented region, and an answer on cross-region routing and where support staff can access the environment from.
- RECOMMENDATION
Start with the managed-API pattern when the workflow is custom but model operations are not the source of competitive advantage; move to private model serving only if evaluation, volume, portability or the data boundary justifies the extra operations. Your stated technical capability is "unknown".
Self-hosted
STRONG ALTERNATIVEOpen-weight models on infrastructure you operate
- ASSESSMENT
Documents, queries and embeddings stay on machines you own, using an open-weight model whose licence you review. For a brief that involves confidential documents, that removes a model-API vendor from the data path rather than governing that transfer by contract.
- ASSESSMENT
It costs you the operational work instead: a GPU server, Docker, Linux, backups and a patching routine. Your stated technical capability is "unknown", which is the attribute this option most depends on.
- ASSESSMENT
No processor agreement, subprocessor list or cross-border transfer assessment is needed for the model itself, because no third party processes the content.
- RECOMMENDATION
Recommended where local processing is preferred (you did not say so) and the content is sensitive (confidential documents).
Enterprise SaaS
CONSIDER IFFinished closed-source cloud product with enterprise controls
- ASSESSMENT
This is a complete vendor application, not a model API: examples include an enterprise assistant, coding copilot or document product with the workflow, interface, connectors and administration already built. It can use closed-source cloud models while requiring no model hosting or application engineering from your team.
- RECOMMENDATION
Choose it when the product already performs the actual workflow and its controls meet your requirements. Do not choose it only because its underlying model is strong: a finished SaaS product is less flexible than building against a managed API when your process, integrations or review steps are organisation-specific.
- ASSESSMENT
Vendor commitments are treated as unverified until we have fetched the page that makes them. Until then this option carries questions to ask, not assurances: a signed data processing agreement covering the data you will actually put in; a documented data residency commitment naming the region, in the contract rather than a blog post; a written no-training commitment for your content, including uploads and connected sources; stated retention periods and a deletion path you can exercise; an administrative audit log you can export, and SSO with group-based access control.
- RECOMMENDATION
No jurisdiction was named, so this is the check rather than the conclusion: compare the vendor's stated processing locations and subprocessor list against the cross-border transfer rules wherever you operate before uploading anything.
03Deployment stacks
04Tools by hosting option
Self-hosted1
Private cloud2
- SaaSHarveyDomain platform for law firms and legal departments covering document review, drafting and research workflows over a firm’s own material, sold with enterprise security commitments.
- Self-hostedRAGFlowRetrieval engine built around deep document parsing: layout-aware chunking of PDFs, tables and scans, citation-backed answers, and a visual pipeline for building knowledge bases.
Vendor cloud2
- SaaSClaudeAnthropic’s assistant, available as a team and enterprise product with SSO, audit logs, project workspaces and commercial terms, and as an API for building applications.
- SaaSHarveyDomain platform for law firms and legal departments covering document review, drafting and research workflows over a firm’s own material, sold with enterprise security commitments.
05Compliance hot spots
This use case usually raises confidentiality, professional secrecy, personal data, model training, retention, human oversight, auditability, automated decision-making, logging.
- Professional secrecy
- No published jurisdiction page names this topic yet
- Model training
- No published jurisdiction page names this topic yet
- Retention
- Hong Kong
- Human oversight
- No published jurisdiction page names this topic yet
- Auditability
- No published jurisdiction page names this topic yet
- Automated decision-making
- European UnionUnited KingdomSouth Korea
- Logging
- European Union
06Example questions
Each of these opens the question box with the text already in it. The answer is researched for your organisation, not for this page.
07Related use cases
- DocumentsSpreadsheet analysisAsking questions of tabular data in natural language — totals, trends, outliers, reconciliations — and getting an answer with the calculation behind it. Works best when the model writes code that runs, rather than reading numbers itself.
- DocumentsRFP draftingAnswering tenders and security questionnaires from a library of previous answers. The work is retrieval plus reuse: most questions have been answered before, in a form somebody already approved.
- DocumentsReport generationProducing a recurring document — a management pack, client update, board summary, compliance return — from data and prior text. The template and the data are fixed; the narrative around them is what takes the time.
- DocumentsOCRTurning images of text — scans, photographs, faxes, historic files — into machine-readable text with layout. The step everything else depends on: no extraction, search or classification pipeline is better than the text layer underneath it.
- DocumentsInvoice extractionReading supplier invoices — header fields, line items, tax, totals — into structured records. The extraction step only. What happens to the record afterwards is invoice processing automation.
- DocumentsDocument classificationSorting incoming documents into types and routing them — which team, which folder, which workflow, which retention rule. Usually the first step of a larger pipeline and the cheapest place to remove manual handling.
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