Safe P&C underwriting AI, delivered for your business
We design, build, and run underwriting AI copilots tailored to your workflows, with real-world evaluation, monitoring, and auditability built in.
Proof
What you improve in 6–12 weeks
Faster time-to-quote
Lower referral burden
Better underwriting consistency
Controlled risk + audit readiness
Underwriting AI solutions we ship
Submission intake + triage
Route and prioritize submissions with AI-assisted triage.
Appetite matching + referral recommendation
Match risks to appetite and recommend referral paths.
Exposure extraction + enrichment
Extract exposures and enrich submissions from documents.
Underwriting copilot
Guideline Q&A and decision support for underwriters.
Document intelligence
Loss runs, SOV, inspections—structured and queryable.
QA + compliance checks
Make decisions easier to justify and safer to audit.
Safety isn't a promise—it's built into the system
Privacy-safe data foundations
Including synthetic data where needed so you can train and evaluate without exposing real PII.
Evaluation before deployment
Real-world tests tied to underwriting outcomes so you ship only what meets your bar.
Monitoring after launch
Drift, performance, and failure modes—so you see issues before they scale.
From pilot to production, end-to-end
Discover
Use case, workflow, and data reality.
Build
Data readiness, model, and human-in-the-loop.
Validate
Evals, thresholds, and sign-off.
Operate
Monitoring, retraining triggers, and reporting.
Powered by
DataFramer
Synthetic + scenario-complete datasets, balancing, labeling, evaluation set creation.
AIMon
Monitoring, evaluations, governance, audit trails, policy controls.
Insurance Advisors
Domain experts who ensure solutions align with your underwriting guidelines, appetite, and workflows.
Dedicated AI Engineers
ML/AI Engineers that design and build your underwriting AI with human-in-the-loop and safety built in.
Built for P&C workflows
Data never leaves your network
What it automates
- Fleet classification and exposure extraction
- Driver and vehicle data extraction from applications
- Loss run summarization and prior loss scoring
Typical inputs
- Applications, MVRs, loss runs
- Fleet schedules, vehicle lists
- Prior carrier data when available
How we keep it safe
- Appetite rules and referral thresholds in the loop
- Evidence and reason codes for every recommendation
What it automates
- Property characteristics extraction
- SOV and schedule parsing
- CAT exposure and location scoring
Typical inputs
- Applications, SOV, inspections
- Loss history, building details
- Geocoding and replacement cost data
How we keep it safe
- Human sign-off on binding and limits
- Audit trail for all extracted fields
What it automates
- Operations and hazard classification
- Exposure base and payroll extraction
- Prior claims and incident summarization
Typical inputs
- Applications, loss runs, SOV
- Classification guides, experience mods
- Certificate and policy data
How we keep it safe
- Thresholds and rules aligned to guidelines
- Reason codes and evidence for referrals
What it automates
- Class code and payroll verification
- Experience mod and loss summary
- Return-to-work and injury type tagging
Typical inputs
- Applications, payroll reports, loss runs
- NCCI/state guides, mod worksheets
- Claims and OSHA data when available
How we keep it safe
- Evals tied to mod accuracy and referral rates
- Monitoring for drift in class mix and payroll
Built for enterprise requirements
- Access controls + audit logs
- Versioning for models and prompts
- Evidence capture for decisions
- SOC2-aligned posture
- Data never leaves your network
Start with a pilot designed to prove value safely
We run a focused pilot in a few weeks: discover your use case and data, build a working workflow with human-in-the-loop, validate with evals and thresholds, and hand you an evaluation report plus a monitoring dashboard so you can operate with confidence.
Pilot at a glance
- Timeline
- 4–8 weeks
- Deliverables
- Working workflow + evaluation report + monitoring dashboard
- Success metrics
- Cycle time Referral rate Hit ratio Leakage
Frequently asked questions
- What data do you need to start?
- We typically start with sample applications, loss runs, and any existing guidelines or referral rules. We can work with messy or limited data and use synthetic or scenario-based data where needed to fill gaps.
- Can this work if our data is messy or limited?
- Yes. We design for real-world data: missing fields, inconsistent formats, and limited volume. We use synthetic data and scenario completion to extend what you have and validate before going live.
- How do you keep humans in control?
- Humans stay in the loop: referral recommendations, binding decisions, and overrides are designed around your workflow. We provide reason codes, evidence, and thresholds so underwriters can trust and correct the system.
- How do you measure performance?
- We tie evaluation to underwriting outcomes: cycle time, referral rate, hit ratio, leakage, and guideline adherence. You get an evaluation report before launch and ongoing monitoring after.
- How do you handle privacy and compliance?
- We use privacy-safe data foundations (synthetic where appropriate), access controls, audit logs, and evidence capture. Our posture is aligned with SOC2 expectations; we can discuss certification status for your requirements.
- What happens after the pilot?
- After the pilot you get a working workflow, evaluation report, and monitoring dashboard. We help you scale to production with retraining triggers, governance, and ongoing support.
Get Started
Let's build safe underwriting AI for your team
Book a free working session or talk to an expert today.