Source-linked legal research
Move from a framed Indian-law question to authorities and proposition-led research that a lawyer can verify.
Legal AI for Indian law firms
Increase matter capacity.
Protect the standard of review.
Gotham helps Indian law firms structure research, review, diligence and litigation work around sources, playbooks and lawyer approval—so the business case includes correction time, confidentiality and client obligations, not only faster first drafts.
Primary valueCapacity, consistency and reusable firm knowledge
Control pointQualified lawyer review remains accountable
First stepOne matter workflow with a measurable finish line
The buying problem
A law firm does not realize value merely because an AI produces text quickly. The output must survive partner review, fit the matter record, respect privilege and client restrictions, and move through the firm’s normal drafting and approval process.
The commercial case also depends on the work type. Reclaimed time may improve turnaround, margin, lawyer development or capacity, but it becomes revenue only when the firm can deploy that capacity responsibly. Evaluate each workflow separately instead of applying a universal productivity claim.
Where Gotham can enter the work
Contract review and redlining
Apply approved clause positions, surface deviations and preserve the agreement language supporting each finding.
Diligence across document sets
Build consistent review tables and issues lists without losing the path back to each source document.
Litigation chronology
Organize dates, actors, issues and conflicting accounts with page- or paragraph-level provenance.
Matter drafting
Prepare structured work product from approved sources, precedents and matter facts for lawyer revision.
Firm workflow governance
Separate matters, permissions, playbooks and review roles while retaining an observable correction trail.
What to require in evaluation
Correction burden
Measure partner and associate correction time alongside generation time and turnaround.
Citation integrity
Require authentic sources, pinpoint support and explicit checking of adverse or later authority.
Client restrictions
Test whether matter-specific AI, residency, provider and retention commitments can be enforced.
Knowledge boundaries
Confirm who can use precedents, playbooks and matter work product across teams and ethical walls.
Daily work surfaces
Validate Word, document-system, email and export behavior on the firm’s actual process.
Commercial realization
Distinguish capacity value, margin improvement, faster service and incremental collectible work.
Align the buying committee before the pilot
Questions to resolve
- Which partner owns professional quality and the decision to expand scope?
- Which client terms restrict models, locations, retention or subprocessors?
- How will information security, risk, knowledge and IT approve the workflow?
- What evidence will finance accept as realized value rather than theoretical hours?
Rollout controls
- Begin with trained reviewers and bounded matter types.
- Require source checking and record material corrections.
- Preserve matter permissions, confidentiality and client-specific rules.
- Expand only after quality and adoption gates are met.
Good first pilot candidates
A useful pilot has bounded inputs, a known reviewer and an output whose quality can be scored. Consider:
- Review one agreement type against an approved clause playbook.
- Research a bounded Indian-law issue against known benchmark authorities.
- Build a chronology from a controlled closed-matter document set.
Start with the workflow, not the licence count.
Use a private assessment to choose the first test and its success measures.