Never used for training.
Policies, claims, and outputs stay in your isolated tenant. We never use customer documents to train models — not for fine-tuning, not for evals, not ever.
You spend the first six hours of every matter reconstructing the policy. The next four writing the opinion — cast, chronology, provision-by-provision — from a blank page. WorkProduct handles both: the compiled policy in minutes, and the coverage analysis drafted with the same specialist protocols you use when you're checking your associate's work.
We onboard a small group each quarter — your policies, your matters, no pitch.
Upload the base policy and every endorsement. Get back a single redlined PDF with each change in context, the AI's reasoning pinned to it, and a citation hash you can verify.
The Aggregate Limit shall be USD 25,000,000 USD 50,000,000 per Policy Year, which amount shall be the maximum amount payable by the Insurer for all Damages on account of all Occurrences taking place during the Policy Period, regardless of the number of Insureds or claims made.
The Aggregate Limit shall be reduced by Loss Adjustment Expenses incurred in connection with covered claims, subject to the Notice Provisions in §7.1 and the reservation set forth in E-008 §3(b).
"Cyber Incident" means any unauthorized access to, disruption of, or compromise of an Insured's computer systems, networks, or data, including but not limited to ransomware, denial-of-service, data exfiltration, and any event triggering a notification obligation under applicable privacy law.
Limits, exclusions, and definitions resolve in the order endorsements were issued. Conflicts are flagged, not silently overwritten.
For every change, read the model's plain-English rationale and confidence before you accept it. Nothing is hidden.
Bookmarked, citation-stable redlined PDF. Every modification traces back to the source endorsement and clause.
Point Coverage Analysis at a compiled policy and a claim file. It produces three structured outputs: CAS Chronology, cast of characters, and a provision-by-provision Coverage Opinion — every conclusion pinned to the clause and the claim page it relies on.
Cast of characters, claim chronology, and provision-by-provision opinion — the same artifacts your team builds by hand, drafted in minutes.
Every verdict pins to the clause and the claim page that supports it. No phantom quotes. No hallucinated cites. Audit the chain in seconds.
Every clause and conclusion is editable. Ambiguity is flagged, not papered over. The judgment — and the signature — stays with the attorney.
The system flags Pollution K's own definitions, Breach Prerequisites, dual jurisdiction issues — the things you check for on the third read — on the first read. You review conclusions, not process.
The Policy Compiler handles reconstruction. The Analysis pipeline runs the specialist checks. You spend your time on actual coverage analysis and client communication — not policy archaeology.
If every coverage attorney saves 3 hours per matter on 50 matters a year, that's multiple millions of dollars in either billable capacity or margin — depending on how the firm bills. Same realization rate, more matters per partner, faster turnaround.
Policies, claims, and outputs stay in your isolated tenant. We never use customer documents to train models — not for fine-tuning, not for evals, not ever.
Every conclusion links back to the exact clause and page. No phantom quotes. No hallucinated cites. The model can't make a claim it can't ground in your document.
The product was designed alongside practicing coverage counsel — not retrofitted from a generic AI chat tool. Lawyer-level first-pass review, with the attorney still in the loop.
We're onboarding a small group of firms this quarter. Each beta firm gets a dedicated setup session using their own policies — not a canned demo.