[{"data":1,"prerenderedAt":49},["ShallowReactive",2],{"ind-detail-legal":3,"ind-work-legal":47,"ill-industries\u002Flegal":48},{"slug":4,"title":5,"icon":6,"summary":7,"challenges":8,"useCases":13,"relatedServices":29,"faq":34,"seo":44},"legal","Legal","shield","AIYatch builds AI for law firms and in-house legal teams: contract review and clause extraction, matter-aware knowledge search with citations, drafting assistants trained on your precedents, intake and conflict-check automation, and eDiscovery support. Systems are designed for confidentiality and privilege, with private deployment options and lawyers reviewing every output.",[9,10,11,12],"Contract review and due diligence are slow, repetitive and expensive.","Precedents and know-how are hard to find across matters and document systems.","Confidentiality and privilege rule out consumer AI tools.","Client intake, conflict checks and time capture add non-billable hours.",[14,17,20,23,26],{"title":15,"text":16},"Contract review and clause extraction","LLM pipelines that extract key terms, compare against playbooks, flag deviations and summarise risk for a lawyer to review.",{"title":18,"text":19},"Precedent and know-how search","A RAG assistant over precedents, memos and matter files that answers with citations and respects matter-level access controls.",{"title":21,"text":22},"Drafting assistants","Generative tools that produce first drafts of standard documents and correspondence from your templates and matter facts.",{"title":24,"text":25},"Intake, conflict checks and CRM automation","Agents that capture enquiries, run preliminary conflict checks against your records and prepare engagement paperwork.",{"title":27,"text":28},"eDiscovery and document classification","NLP models that classify, deduplicate and prioritise documents for review, reducing manual first-pass effort.",[30,31,32,33],"rag-systems","nlp","generative-ai-llm","open-source-ai",[35,38,41],{"q":36,"a":37},"Can legal AI run entirely on our own infrastructure?","Yes. We deploy open-source models on your cloud tenancy or on-premise servers so documents never leave your control. Where a hosted model is preferred for quality, we use enterprise contracts with no-training terms and document the data flow for your risk committee.",{"q":39,"a":40},"How do you reduce the risk of AI hallucinations in legal work?","Every answer is grounded in retrieved documents with citations, so the source is one click away. We restrict the assistant to your corpus, add guardrails on unsupported claims, and evaluate accuracy on real questions from your lawyers before launch. Outputs are drafts for review, not advice.",{"q":42,"a":43},"What legal tasks give the fastest return on AI?","Contract review against a playbook, precedent search and first-draft generation of routine documents. These are high-volume, well-defined and easy to measure in hours saved per matter. They also build the document corpus and access controls that later use cases, such as due-diligence review and knowledge search across practice groups, depend on.",{"title":45,"description":46},"AI for Legal Teams: Contract Review & Drafting | AIYatch","Confidential AI for legal teams: contract review and clause extraction, precedent search with citations, drafting assistants, intake automation and eDiscovery support.",[],"\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cline x1=\"260\" y1=\"126\" x2=\"260\" y2=\"380\"\u002F>\u003Cline x1=\"180\" y1=\"380\" x2=\"340\" y2=\"380\"\u002F>\u003Cline x1=\"130\" y1=\"150\" x2=\"390\" y2=\"150\"\u002F>\u003Cline x1=\"130\" y1=\"150\" x2=\"100\" y2=\"250\" stroke-width=\"3.5\"\u002F>\u003Cline x1=\"130\" y1=\"150\" x2=\"160\" y2=\"250\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M174 250A44 44 0 0 1 86 250\"\u002F>\u003Cline x1=\"390\" y1=\"150\" x2=\"360\" y2=\"250\" stroke-width=\"3.5\"\u002F>\u003Cline x1=\"390\" y1=\"150\" x2=\"420\" y2=\"250\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M434 250A44 44 0 0 1 346 250\"\u002F>\u003Ccircle cx=\"260\" cy=\"150\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Cpath d=\"M470 150H560L580 170V300H470Z\"\u002F>\u003Cpath d=\"M560 150V170H580\"\u002F>\u003Cline x1=\"490\" y1=\"186\" x2=\"560\" y2=\"186\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"490\" y1=\"204\" x2=\"550\" y2=\"204\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"490\" y1=\"222\" x2=\"560\" y2=\"222\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"490\" y1=\"240\" x2=\"540\" y2=\"240\" stroke-opacity=\"0.45\"\u002F>\u003Crect x=\"484\" y=\"212\" width=\"80\" height=\"18\" rx=\"4\"\u002F>\u003Ccircle cx=\"555\" cy=\"280\" r=\"12\"\u002F>\u003C\u002Fg>\u003Ccircle cx=\"555\" cy=\"280\" r=\"6\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973682375]