While others build thin wrappers, we engineered a production-grade forensic core for document-intensive insurance litigation. Founder-led, bootstrapped, with live client deliveries already shipped — and a moat that compounds with every anonymized engagement.
Verification-first engineering: more test and adversarial-attack code than engine code. Every emit surface faces a simulated opposing-counsel cross-examination before it ships. But the code is only the floor — the ceiling compounds.
Two verticals live in production on real Ontario claim files. One shared engine; insulated architecture that extends to any regulated insurance line without touching the sealed ones.
Each de-identified engagement feeds anonymized case-pattern intelligence back into detection — operator-reviewed before anything lands. By the fiftieth engagement, the gap is structural.
The recon pass (from $1,500) converts drawer files into billable legal activity that could not exist before. Many small, high-margin transactions — no concentration risk.
This engine was not born in a lab. It was born the day an insurer brushed off a legitimate claim and its policyholder — a tradesman who builds things for a living — decided to read the entire file himself, line by line, and hold it to its own record.
One brushed-off claim became a self-directed forensic education: thousands of hours investigating insurance claims, the statutory and regulatory duties insurers owe when they handle them, and the court decisions where those duties were tested. The founder learned to build a litigation file the hard way — by reading every page and verifying every fact against its source.
When general-purpose AI tools were brought in to help, they failed the only test that matters in litigation: fidelity to the record. They hallucinated — dates shifted, facts changed, key details were overstated in one draft and understated in the next. Every output had to be re-verified line by line, and every redraft introduced new errors. A tool that must be checked against the entire record provides no leverage at all.
So one night, the founder started writing code. Everything AI was supposed to do — and every safeguard a regulated legal offering demands — was engineered in from the first line: verification before generation, source anchoring on every finding, Law Society of Ontario professional-oversight standards, and Canadian privacy and data-residency compliance by construction. Measure before you cut. Verify before you ship. Sign your work.
The original instinct was to arm self-represented litigants. The market said otherwise. The highest-leverage customer is the law firm — equipped with an engine that dismantles document dumps, over-redactions, and withheld productions at machine speed, and converts previously unviable files into billable, source-anchored litigation positions.
Capital raised is governed by a structural, pre-committed framework — not discretion. The rules are set before the money arrives.
Insurance companies have spent 20 years digitizing their claims operations. Every adjuster email, internal memo, position shift, and delay is on record. The first engine that reads that record faster and more precisely than any human team wins the file — and the relationship — permanently. That engine exists. It is live. And the corpus it is building right now cannot be replicated by a competitor starting today.
The Ontario plaintiff bar currently turns away the majority of sub-$20k insurance disputes because the economics do not work. The recon changes the economics on every file. Each converted file is net-new legal revenue that did not exist before the engine ran.
Each de-identified engagement adds doctrinal fingerprints, adjuster-pattern intelligence, and threshold calibration that makes every subsequent run more precise. Bloomberg built its terminal moat over 40 years of market data. This corpus builds faster — and with legal defensibility baked in at every layer.
Auto and title are live proof of concept, not the ceiling. The insulated-branch architecture means each new insurance vertical — property, marine, surety, professional liability, US title — is a configuration on the same engine. The infrastructure cost is already sunk. The next vertical is margin.
Insurance file mechanics mastered from the outside in, then industrialized. The core was forged and battle-tested on real-world files and built for total objectivity — the same standard a tradesman applies when the work bears their name. The mission is simple: industrializing file accountability.
Bootstrapped to live production with client deliveries already shipped — revenue capacity before a single dollar of institutional capital. The end state of the platform is entirely self-serve: cleared firms run the engine natively and verify their own findings. The founder's focus remains structural: absorbing firm feedback, optimizing detection accuracy, and engineering ahead of industry shifts.