Trials are rare. Cases are won or lost in the pretrial phase: building the record, meeting deadlines, drafting discovery, and framing the motions that decide whether a case survives at all. What happens when you run the pretrial phase as a machine? Measured on real matters, the same work gets done at roughly one-tenth the cost, or ten times the output, and often both, with a documented error record that beats both the leading legal-AI tools and human baselines. AI drafts; you file. AI suggests; you decide.
Why quote pro se numbers to attorneys
A business cannot represent itself in court; a corporation must appear through counsel, so in commercial litigation the only pro se party is a lone individual, and they almost never litigate one to a conclusion. Pro se plaintiffs win about 3% of final judgments in federal court. Across federal diversity commercial cases from 2021 to 2026 (cases that by law put more than $75,000 at stake), a self-represented party reached a pretrial conference in only about 1 in 7 of their cases, and trial in roughly 1 in 75 (an analysis of the Federal Judicial Center Integrated Database). Against that, one non-lawyer used this platform to carry multiple complex, multi-defendant business matters through pleadings, counterclaims, and into discovery, pro se. The point was never the litigant. It is the machine.
The IMPACT method
- Ingest the full record into one private, searchable system on local hardware.
- Map every document to the legal element it proves, on intake.
- Pressure-test the record: gap analysis drives the next round of discovery.
- Adversarially review before filing: separate gates, each catching what the others miss.
- Cite to source: every assertion traces to a Bates-stamped page, a statute, or a prior filing.
- Track every deadline and filing, each carrying the rule that generated it.
The error record
A fabricated citation reaching a judge is the error that draws sanctions and ends cases. The leading legal-AI research tools still hallucinate: Westlaw's AI more than 34% of the time and Lexis+ AI more than 17% (Stanford RegLab and HAI, 2024), and more than 1,000 U.S. court filings have been caught citing fabricated AI cases (Damien Charlotin's AI Hallucination Cases Database). Across roughly 40 accepted filings on two active state-court dockets, no fabricated case, invented statute, or made-up legal authority has ever reached a filed document. The two errors that did reach the docket, a wrong-matter caption and a citation, were corrected by formal errata the court accepted.
Case studies
Zero to a filing-ready package in about eight working hours. Running the IMPACT method end to end on one employment matter: more than 7,800 emails ingested and made searchable, a four-count petition, seven exhibits, and a served preservation demand, assembled into a filing-ready 108-page package.
The warranty fight. A single operator fired a complete consumer pre-suit campaign (statutory notice, demand for cure, agency complaints, executive demand letters) through a certified-mail API in minutes. Within eight days, a denied warranty claim was reversed: on a roughly $14,000 engine, the carrier covered everything but $52.
Measure the gain the way your controller would. The number that counts is the one your finance function can verify on your own books: your realized rates against your recorded hours, not an outside estimate.
The five things lawyers say about legal AI, and how this answers each
“It hallucinates and I can't trust the citations.”
The adversarial-review gate plus source-parity checking. Every citation traces back to the exact source document, Bates-stamped if you want it and tracked either way, down to the exhibit number, and across roughly 40 accepted filings on two active dockets, no fabricated citation has reached a filed document.
“It's generic, not built for how I actually practice.”
It is built to your practice, not the other way around. We shape the pipeline and the workflow to how you actually litigate, encode the local rules and conventions of your courts, and add or change functionality on request, immediately. No SaaS roadmap, no feature committee.
“It's a shelf of disconnected tools, and the gains get lost across tabs.”
One command center. Ingestion, evidence mapping, discovery, deadlines, exhibits, and filing live in a single system, which is exactly the integration depth the research says predicts satisfaction four times better than any feature.
“The pricing is opaque and it nickel-and-dimes per feature.”
A transparent operating-system install, not a per-click meter. Embedding and searching hundreds of thousands of documents runs locally at no per-query cost; you pay tokens only for the final drafting a frontier model does over the few passages that matter.
“There's no defensible record when I need to prove something.”
Every production is Bates-stamped with a full chain-of-custody audit trail, every assertion traces to source, and the code gates leave an auditable trail of what was checked.
The offer
AI Litigation Support Consulting. A free 30-minute consultation, then hourly private consulting. It stays private: every engagement comes with a simple one-page mutual NDA, I never reveal a client's name (including for marketing), and references are written opt-in only. The Free-Case Challenge: give the system one live case to ingest and build out, and if it does not beat your three best paralegals combined on speed, accuracy, and quality, you owe nothing. Wait for the legal-AI tool you already pay for to put the function you need on its roadmap, or be in complete control now, more effective, higher quality, and without the hallucination risk those tools carry on the studies' own numbers.