Contract review copilot
60–80% ↓
first-pass review time
Surface risky clauses, missing protections, and deviations from standard templates. Attorney stays in control — the copilot does the hunt-and-flag work, redlines stay on humans.
Legal
AI for legal teams that won't tolerate hallucinated citations.
AISD builds AI for law firms and in-house legal teams — contract review, due diligence agents, eDiscovery acceleration, legal research, drafting copilots, compliance monitoring. Privilege-aware, citation-disciplined, on-prem deployment available.
6 proven use cases · 4–9 mo typical payback · On-prem & VPC available
Use cases
Six places AI earns its keep at a law firm.
Due diligence agent
3–5×
data-room throughput
Ingest data rooms, extract key terms, surface red flags across thousands of docs, generate diligence memos. M&A, real estate, and litigation use cases.
eDiscovery + review acceleration
40–60%
review cost reduction
AI-assisted review with privilege detection, responsiveness coding, and quality control. Defensible workflows with audit trails.
Legal research agent
2–3×
research throughput
Cite-checked research across case law, statutes, and secondary sources. Citations always returned with answers — no hallucinated cases.
Drafting assistants
30–50% ↓
first-draft time
Briefs, motions, contracts drafted from prompts + firm-specific examples. Attorney refines; AI accelerates first pass. Brand voice preserved.
Compliance + regulatory monitoring
24/7
regulatory tracking
Track regulatory changes across jurisdictions, summarize impact on the firm's clients, draft client alerts. Especially valuable for highly regulated practice areas.
Confidentiality & citation discipline
Built for the constraints legal practice actually has.
Privilege, confidentiality, malpractice exposure, and the citation-discipline problem that takes generic LLMs out of the running for legal work. We build for these from day one.
Privilege + confidentiality protection
Privileged and confidential client data never leaves your perimeter when configured for it. We build with on-prem and VPC deployment from day one for sensitive practices.
Citation discipline + hallucination guards
Every legal claim returned with a citation. Citations validated against authoritative sources. Hallucinated case law is a known failure mode of generic LLMs — we engineer against it.
Audit trail for malpractice + regulator review
Every model call logged with inputs, outputs, and rationale. Exportable for malpractice review, bar inquiries, and internal QC.
Human-in-the-loop on advice
AI surfaces evidence, drafts language, and accelerates research. Attorney signs off on advice. Models don't practice law; attorneys do.
Services for legal
Adjacent industries
Frequently asked
Common questions.
What AI use cases actually work for law firms today?
Six proven use cases. Contract review copilots cut first-pass review time 60–80% by surfacing risky clauses and template deviations. Due diligence agents process data rooms 3–5× faster, generating structured diligence memos. eDiscovery acceleration with privilege detection and responsiveness coding cuts review costs 40–60%. Legal research agents return cite-checked answers across case law and statutes (no hallucinated cases). Drafting copilots produce 30–50% faster first drafts of briefs and contracts. Compliance monitoring tracks regulatory changes 24/7 across jurisdictions.
How do you prevent hallucinated case law in legal AI?
Three engineering layers. Retrieval-augmented generation forces the model to cite from a vetted corpus of authoritative sources (case law databases, statutes, internal precedent). Citation validators check every returned citation against the source database — if a citation isn't there, it's flagged. Output schemas require citations alongside every legal claim; answers without citations are rejected before reaching the attorney. Generic LLMs hallucinate cases; engineered legal AI doesn't.
How does AISD handle privilege and confidentiality?
Standard pattern: on-prem or VPC deployment with no data leaving the firm's perimeter. Open-weight models (Llama, Mistral) run on dedicated infrastructure. Field-level audit logging exports for malpractice review. Data minimization at every boundary — agents see only the matter they're working on, never cross-matter context. Privilege flags propagate through the system; privileged documents are tagged and access-controlled separately.
Does AISD's legal AI replace attorneys?
No. Models don't practice law; attorneys do. Our legal AI accelerates the unglamorous work — first-pass review, research synthesis, drafting, due diligence, document classification. Attorneys retain judgment, advise clients, and sign every output. The ROI comes from reallocating attorney time from rote work to higher-value advisory and litigation strategy.
What is an AI agent?
An AI agent is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop. Unlike a chatbot — which responds and waits — an agent acts, observes the result, and decides what to do next, often across dozens of steps.
How is AI consulting different from AI development?
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.