Legal AI is high-stakes: a hallucinated case citation is a malpractice incident. NKKTech builds legal AI with the engineering discipline that domain demands — retrieval-grounded answers, jurisdiction-aware retrieval, privilege-aware data handling, and faithfulness scoring at 0.95+ on every release. Used by mid-market US law firms, UK barristers' chambers, and LegalTech startups across the US, UK, Singapore, and Australia.
Six capability patterns we ship for law firms and LegalTech. All architected around retrieval grounding (no free-form legal opinion generation) and audit-readiness.
Clause extraction, deviation-from-template detection, risk scoring. Fine-tuned per firm's playbook. Lawyer reviews flagged items; AI handles the 80% that's boilerplate.
Predictive coding for first-pass review, semantic search across millions of documents, privilege detection. Reduces review hours 60–80% with documented chain of custody.
Case-law retrieval grounded in Westlaw/LexisNexis or open sources (CourtListener, Caselaw Access). Jurisdiction-aware, citation-correct, faithfulness ≥0.95.
Memo drafts, brief drafts, motion templates. Pre-loaded with firm's house style and clause library. Always lawyer-in-the-loop; outputs are starting drafts, not final filings.
Initial-consultation triage bots, conflict-check automation, intake-form intelligence. Privacy-first; no client data leaves your VPC.
Internal know-how search across past matters, briefs, and memos. Privilege-aware retrieval that respects matter-level access controls.
1–2 weeks. Map data flows; identify privileged communications and work-product; design access controls before any AI is built.
1 week. Scope a narrow MVP (one matter type, one clause set, one office) for the pilot. Avoid "boil the ocean" scope.
4–8 weeks. Build the system; partner with 2–3 lawyer reviewers for continuous eval; iterate on retrieval and prompting.
2–4 weeks. Phased rollout, time-savings measurement, attorney-trust survey, ongoing eval framework handoff.
Due diligence document review, deal-precedent search, clause-bank automation, redlining assistants.
E-discovery, deposition transcript analysis, brief-drafting copilots, witness preparation Q&A.
Title search summarization, lease-clause extraction, zoning + regulatory lookup.
Policy review, employment-contract template generation, regulatory compliance Q&A.
Prior-art search, trademark-clearance Q&A, patent-claim-language drafting.
AI features inside existing LegalTech products — review automation, search upgrade, document intelligence APIs.
Privilege is treated as the highest-sensitivity data class. Privileged communications are stored separately from non-privileged work-product; access controls enforce matter-level restrictions; LLM calls touching privileged content go only to BAA-covered providers (Azure OpenAI Enterprise, AWS Bedrock with BAA, self-hosted) and never to public APIs. Every privileged-data access is logged with attorney + matter attribution.
No. Every output is positioned as a draft or research starting point, with explicit "for attorney review" tagging in the UI. We refuse use cases that would put generated content in front of a client without attorney review.
Single biggest risk in legal AI, and we engineer around it specifically. Every citation in a generated output is verified against the source database before display. Unverified citations are flagged or removed. We hold ourselves to faithfulness ≥0.95 (LLM-as-judge) and citation-precision ≥0.99 (verifier check). Below those targets, the system is held back from production.
Yes — iManage, NetDocuments, Clio, PracticePanther, SmokeBall, custom APIs. We integrate as a layer on top; matter records and permissions stay in the existing system of record.
USD 50K–150K for a typical 8–14 week pilot covering one practice area or use case. Larger firm-wide rollouts run 6–12 months and USD 200K–800K. Recurring LLM cost depends on volume; typical mid-firm spend is $1,500–$8,000/month.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Last updated: · Reviewed quarterly for accuracy.
30-minute free discovery call with a senior NKKTech engineer (not a sales rep). We'll review your requirements, scope an engagement, and tell you honestly whether we're the right fit.
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