AI in Litigation Support
Timeline extraction, deposition analysis, exhibit classification, chronology building, and the AI systems that help litigators prepare and try cases.
Timeline extraction, deposition analysis, exhibit classification, chronology building, and the AI systems that help litigators prepare and try cases.
Regulatory change detection, gap analysis automation, policy compliance checking, and building AI systems that track regulatory requirements across jurisdictions.
Clause extraction, obligation detection, risk identification, and building NLP systems for commercial contract analysis at law firm and enterprise scale.
e-Discovery, technology-assisted review (TAR), predictive coding, and building ML systems that process millions of documents for legal discovery in weeks instead of years.
Why LLM hallucination is malpractice in legal contexts, grounding strategies, citation verification pipelines, and architecture patterns for trustworthy legal AI.
Patent analysis, prior art search, trademark similarity detection, and the ML systems that support patent prosecution, portfolio management, and IP litigation.
Domain adaptation of LLMs for legal tasks - LegalBench evaluation, instruction tuning on legal data, and building legal AI models that outperform general-purpose LLMs on specific tasks.
Dense retrieval over case law, citation graph analysis, precedent finding, and building legal research AI that surfaces relevant authorities without hallucinating fake cases.