AI Workflow Automation for Legal

LegalTech lives or dies on one thing generic AI gets catastrophically wrong: citations. After the Mata v. Avianca sanctions — where a lawyer filed a brief citing six fabricated cases hallucinated by ChatGPT — no serious legal product can surface an authority it cannot trace to a real reporter. SpeedMVPs builds legal research and drafting copilots on retrieval-augmented generation (RAG) pipelines where every proposition is grounded in retrieved source text and every cited case, statute, or regulation is verified against a real corpus (CourtListener/RECAP, PACER dockets, the Federal Register, or your firm's licensed Westlaw/Lexis/vLex feed) before it ever reaches the user. We render pinpoint citations with a click-through to the underlying passage, flag any unsupported sentence, and keep a human attorney in the loop by design — because in law the failure mode isn't a bad answer, it's a confident wrong one that gets someone sanctioned under FRCP Rule 11.

The regulatory and ethical surface for a legal AI product is unlike any other vertical, and we build to it from day one. Attorney-client privilege and the work-product doctrine mean prompts and documents can never leak into a third-party training set — so we default to enterprise LLM endpoints with zero-retention, no-training contractual terms, or self-hosted open-weight models on your own VPC when the matter demands it. ABA Model Rule 1.6 (confidentiality), Rule 1.1 Comment 8 (the duty of technology competence), and Rule 5.3 (supervision of non-lawyer assistance, now read to cover AI) shape the guardrails; unauthorized-practice-of-law (UPL) risk shapes the UX so the tool assists a licensed attorney rather than dispensing legal advice to end users. We ship SOC 2-aligned controls — encryption at rest and in transit, per-matter access scoping, and an immutable audit log of every AI action — so your GC and malpractice carrier can actually approve the thing.

Contract intelligence is where most funded LegalTech teams start, and it rewards real domain modeling over a thin GPT wrapper. We build clause extraction and obligation-mining pipelines that identify indemnification, limitation-of-liability, assignment, change-of-control, auto-renewal, and governing-law provisions, then diff them against your clause library or a playbook of fallback positions for automated redlining. For high-volume M&A and diligence, we structure the output the way a deal team actually consumes it — an issues list, a rights-and-obligations matrix, and a chronology — and pipe it into the systems of record (iManage, NetDocuments, or a SharePoint DMS) rather than a dead-end dashboard; it's the same extract-obligations-then-route-to-system-of-record pattern behind the enterprise procurement document agents we detail in the Apex Enterprises case study, retargeted at legal instruments. Because these documents are unstructured and idiosyncratic, we combine layout-aware parsing (OCR for scanned exhibits, table and signature-block detection) with LLM extraction and a confidence score, so reviewers triage low-confidence spans instead of re-reading everything.

E-discovery and litigation tooling carry a defensibility bar that off-the-shelf AI simply ignores. Technology-assisted review (TAR) has been judicially blessed since Da Silva Moore, but it is only defensible if the process is documented, sampled, and reproducible — so when we build predictive-coding or privilege-screening features we align to the EDRM stages, preserve chain-of-custody metadata, and generate the statistical validation (recall/precision on a control set, elusion testing) that a party has to defend in a meet-and-confer under FRCP Rule 26(f). We handle the practical plumbing too: load files and production numbering, Relativity-style review workflows, deduplication and email threading, and redaction pipelines that burn PII/privileged text rather than merely hiding a layer. The goal is a tool a litigation-support team can stand behind in front of a magistrate, not a black box.