How Vidhaana's AI Reviews Contracts in Minutes, Not Days
Traditional contract review forces legal teams to manually read through every clause, cross-reference defined terms, and flag deviations from approved templates. A single NDA might take 30 minutes; a complex vendor agreement or SPA can consume an entire day. Vidhaana's contract review engine ingests the full document, parses it into a structured clause-level representation, and runs over 200 pre-trained risk checks against it — all within seconds of upload. The system identifies non-standard indemnity caps, unusual limitation of liability carve-outs, missing governing law clauses, and ambiguous force majeure definitions. It then produces an annotated risk report ranked by severity so your team can focus on what actually matters.
The AI does not simply keyword-match. It uses contextual understanding to distinguish between a standard mutual confidentiality obligation and a one-sided confidentiality clause that exposes your client to downstream liability. It flags when a non-compete radius exceeds market norms, when an automatic renewal window is shorter than the notice period required to terminate, or when an IP assignment clause lacks a carve-out for pre-existing IP. These are the kinds of issues that junior associates miss and senior partners catch too late — Vidhaana catches them at first pass.
Clause Extraction, Comparison, and Redlining
Beyond risk identification, Vidhaana extracts every material clause — payment terms, termination triggers, representations and warranties, dispute resolution mechanisms, data protection obligations — and presents them in a structured summary table. You can compare any uploaded contract against your approved playbook or a counterparty's previous version, with differences highlighted inline. The comparison engine handles reordered clauses, renumbered sections, and even semantically equivalent language expressed differently. When negotiating, your team can generate a redlined version directly from the platform, with suggested alternative language drawn from your organization's clause library.
- Extracts 40+ clause types including indemnity, liability caps, IP assignment, termination, non-solicitation, and data processing obligations
- Side-by-side comparison against playbooks, previous versions, or industry templates with semantic diff highlighting
- Automated redline generation with alternative clause suggestions sourced from your approved clause library
- Multi-language support covering English, Hindi, Kannada, Tamil, and 12 additional languages with cross-language clause matching
- Bulk review mode processes up to 500 contracts per batch — ideal for portfolio-wide audits or post-acquisition reviews
- Integration with Microsoft Word, Google Docs, and CLM platforms via API for seamless workflow embedding
Built for Indian and Cross-Border Contracts
Indian contracts carry unique complexities — stamp duty requirements that vary by state, compliance with the Indian Contract Act 1872, mandatory arbitration seat specifications under the Arbitration and Conciliation Act 1996, and TDS implications embedded in payment clauses. Vidhaana's models are trained on Indian legal language and regulatory requirements, not just common law boilerplate. For cross-border agreements, the system flags jurisdictional conflicts, identifies clauses that may be unenforceable under Indian law (such as non-compete provisions exceeding reasonable scope under Section 27 of the Indian Contract Act), and checks data transfer clauses against the Digital Personal Data Protection Act 2023 requirements.
Whether your team handles 50 contracts a month or 5,000, Vidhaana eliminates the bottleneck of first-pass review. Associates spend their time on judgment calls and negotiation strategy rather than reading boilerplate. General counsel get dashboard-level visibility into contract risk exposure across the entire portfolio. And the system learns from your feedback — every accepted or rejected suggestion refines the model for your organization's specific risk appetite and drafting preferences.
