A new paradigm for insurance technology
Every approved product filing should become a working, tested, deployable application — without a developer ever touching it. That's not a pitch. It's built. It's running. You can try it right now.
The Paradigm Shift
"We dump in an approved filing, an agent digests it, makes the changes, and then someone looks at the testing output once it is done to approve it. We don't need any other steps."
The Proof
Submit the same application in Georgia and Minnesota. Watch the system produce entirely different outcomes — different plans, different rating, different underwriting paths — driven by rule documents alone.
Pick a real product filing from SERFF. View the raw source PDFs. Watch the system build a working application and process it through nine agents — all in one view.
Launch the demo →Medicare Supplement, Critical Illness, Dental, Accident, Hospital Indemnity, Short-Term Disability. Same engine. Different rule documents. Different behavior.
See all products →Not toy data. Actual SERFF filings from six different carriers — real rates, real forms, real underwriting rules.
See the filings →The Architecture
Not everything needs AI. Not everything should be hardcoded. The architecture uses each tool where it's strongest.
AI agents read plain English rule documents. A business analyst edits a document, not a codebase. No developer ticket required.
No LLM should do math with real money. Premiums are exact to the penny. OEP windows are calculated, not reasoned about.
Deterministic code calculates dates. LLM validates reasoning against federal rules. Each does what it's best at.
Every decision is logged. Every agent explains its reasoning. A validation agent reviews the entire chain for contradictions. When input data is unclear, the system diagnoses the gap — it doesn't guess. This isn't confidence scoring. It's clarity diagnostics.
The Argument
A new state filing goes from approved to deployable in hours. Not weeks. Not months. The filing is the input. The application is the output. The bottleneck is eliminated.
Rate change? Update a JSON table. Rule change? Edit a plain English document. Regulation change? A coding agent interprets and implements. No developer sprint for any of it.
Every application self-tests. Every decision has an audit trail. When a DOI examiner asks "why did you decline this applicant?" — the system produces the exact rule, exact input, exact reasoning.
Build for 2030 pricing, not 2026. Inference costs are dropping. Local models run on existing hardware at near-zero marginal cost. The architecture assumes cheap inference as the default.