Legal AI software in India
starts with the category.

The short answer

Choose a legal workspace when research, drafting, document analysis and matter execution must share context. Choose a legal content platform when authoritative research coverage and know-how dominate the problem. Choose AI contract review when the immediate constraint is finding and comparing contract language. Choose CLM when the business needs end-to-end control from request through renewal.

Products increasingly cross those boundaries. That does not make the boundaries irrelevant. A team should identify the authoritative contract, document and matter systems before it evaluates overlapping AI features. The companion guide to AI contract review versus CLM applies that method to contracting.

Four categories that should not be collapsed

Category labels describe public positioning, not a complete feature inventory. A platform may span several columns. Verify whether each required capability is included, configured, separately licensed, professional services or roadmap.

A representative shortlist, not a leaderboard

The following products illustrate different system boundaries visible in the Indian market. The “test closely” column is an evaluation recommendation, not a negative finding.

Gotham focuses publicly on Indian courts, tribunals, regulators, statutes and India-oriented legal execution. Harvey and Legora publish broader global legal-workspace positions. Thomson Reuters positions CoCounsel Legal around Westlaw, Practical Law and customer data. SpotDraft positions itself as AI-native CLM from request to renewal. A public description is a starting point; source entitlement, local workflow depth and the delivered configuration must be demonstrated.

Which India-specific questions change the shortlist?

Primary and secondary source coverage

Ask for a current source schedule by court, tribunal, regulator, legislation type, date range and update method. Test citation resolution, pinpoint support, later treatment and amendments. A statement that a system supports “India” does not reveal the depth, currency or entitlement of the corpus.

Procedural and drafting fit

Demonstrate the actual output: a court-format document, Indian regulatory note, contract issue table, diligence report or research memorandum. Confirm whether local formatting, stamps, signatures, filing portals and state-specific questions sit inside the product or remain external steps.

Data location and transfer

Map prompts, files, embeddings, logs, backups, support access, subprocessors and model providers. Obtain advice on the organisation's obligations and client terms. Vendor headquarters do not determine where every component processes data.

Language and document reality

Use poor scans, tables, stamps, annexures, mixed scripts, amendments and the file formats found in real matters. Record extraction failure separately from a legally incorrect answer.

What should every legal AI vendor prove?

1. The exact sources, products, model providers and regions in the proposed configuration.
2. Source traceability and correction behaviour on representative Indian work.
3. Matter, role, ethical-wall, sharing and administrative controls.
4. Customer-data use, retention, deletion, support access and subprocessor terms.
5. Word, document-system, contract-system and identity integration behaviour, including permission mapping.
6. Human-review paths, audit evidence, incident response and a workable manual fallback.
7. Complete pricing for licences, usage, connectors, implementation, support and exit.

Use the legal AI evaluation scorecard to separate mandatory gates from weighted preferences. Model acquisition and operating costs with the legal AI ROI calculator and the legal AI TCO guide.

How to run a fair pilot

1. Choose one bounded workflow, not a general assistant demo.
2. Freeze the same permitted inputs, authorities, playbook and expected output for every product.
3. Use the same qualified reviewers and define critical failures before testing.
4. Score source support, material omissions, correction time, workflow completion and downstream usability.
5. Test permissions, export, deletion, integration failure and support alongside output quality.
6. Record every claim as observed, contractually committed, publicly documented or unverified.

If the organisation is considering internal development as well, apply the same acceptance criteria using the legal AI build-versus-buy framework. Do not exempt an internal prototype from security, quality or lifecycle scrutiny.