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At ILTACON, Litera will preview how deterministic redlines, firm-approved precedent, and agentic change management work together
CHICAGO - illiNews -- Litera (https://www.litera.com/?utm_source=media&ut...), the Legal AI platform provider that best unifies the practice and business of law, announced new capabilities that connect contract drafting and negotiation in one trusted Legal AI workflow. From sourcing firm-approved precedent and building the first draft to comparing versions, understanding risk and proposing response language, Litera keeps the work connected instead of forcing lawyers across separate tools. The announcement includes new Lito (https://www.lito.app/) workflows, Litera's award-winning Legal AI agent, in Microsoft Word and Litera for Windows, a modern desktop experience that brings Litera's Legal AI platform directly to a user's computer.
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Litera is the only Legal AI provider that combines deterministic accuracy for high-stakes work with modern LLM intelligence, delivered through one agent and built on 30 years of legal-specific expertise. Most Legal AI platforms rely primarily on probabilistic foundation models. Even when fine-tuned for legal work, outputs can vary and require extensive verification. That creates the Trust Gap: AI promises speed, but the value disappears when lawyers must recheck every result. Litera closes that gap by applying the right AI to each task. Its proprietary deterministic engines provide repeatable, verifiable results where precision matters. LLM-powered intelligence builds on that foundation to surface insights, accelerate workflows, and handle complexity that rules alone cannot reach.
"Negotiation is where a matter is won or lost, and where a single missed change carries the most risk," said Adam Ryan (https://www.linkedin.com/in/adamryanadvisory), Chief Product Officer, Litera.
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Litera is the only Legal AI provider that combines deterministic accuracy for high-stakes work with modern LLM intelligence, delivered through one agent and built on 30 years of legal-specific expertise. Most Legal AI platforms rely primarily on probabilistic foundation models. Even when fine-tuned for legal work, outputs can vary and require extensive verification. That creates the Trust Gap: AI promises speed, but the value disappears when lawyers must recheck every result. Litera closes that gap by applying the right AI to each task. Its proprietary deterministic engines provide repeatable, verifiable results where precision matters. LLM-powered intelligence builds on that foundation to surface insights, accelerate workflows, and handle complexity that rules alone cannot reach.
"Negotiation is where a matter is won or lost, and where a single missed change carries the most risk," said Adam Ryan (https://www.linkedin.com/in/adamryanadvisory), Chief Product Officer, Litera.
- Start from firm-approved precedent: Available now, Precedent within Litera Create Content lets lawyers search the firm's curated content library and document management system for gold-standard clauses, definitions, and precedent documents. Lawyers build first drafts from language the firm has already approved and negotiated successfully.
- See exactly what changed: When the opposing side returns a new version, Litera's deterministic comparison engine shows every change in a redesigned unified viewer across browser and desktop. Litera for Windows adds right-click comparison from Windows Explorer and direct redline sharing through Outlook. Comparison also sits within Lito, keeping the redline in the same Legal AI experience where the negotiation continues.
- Act on what matters: Beginning in late September, Lito's Manage Changes workflow in Word will build AI-powered change management on top of Litera Compare's (https://www.litera.com/products/litera-compare) deterministic redline. It will surface changes that carry risk, explain the impact of each one, and suggest rewrites, so lawyers spend time on consequences rather than spotting edits. Grounding those suggested rewrites in the firm's own approved precedent is planned for Q4.
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Source: Litera
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