Underwrite faster without losing control of risk
Move from slow manual reviews and inconsistent risk signals to faster quotes, stronger pricing confidence, and human-controlled
underwriting decisions.
What changes with Bernoly
Underwriters spend too much time on routine risk checks
Before a quote can be issued, underwriters often need to review customer details, claim history, vehicle or property information, location data, documents, and internal rules across several systems. Bernoly helps insurers turn this routine evaluation into a structured human-AI workflow. AI agents can collect, enrich, and pre-check key risk factors, while underwriters focus on exceptions, low-confidence cases, and decisions that require expert judgment.
Rating decisions become inconsistent across cases and teams
Most carriers never measure how far two underwriters diverge on the same risk. the files never sit side by side. Our Intelligence Workflow Architecture paper points to the study Kahneman documents in Noise: two underwriters at one company priced the same risk a median of 55% apart, while their own executives guessed the gap was 10%.Bernoly removes the invisibility, not the underwriter. Rating logic, risk signals, and comparable past decisions land in one view before pricing and when a human makes the call, AI agents check it against how the rest of the book priced similar risk. Either it's consistent, or it stands as a deliberate exception, in the open, with a reason attached.
Most of what tells you the real exposure isn't in the submission
A submission only tells you what the applicant chose to disclose. The real exposure usually sits elsewhere. public records, the business's own website and marketing material, online reputation, and specialist risk feeds. Bernoly's agents pull these together and connect to the location and risk-data providers you already trust, rather than trying to replace them. Instead of an underwriter chasing each source by hand, the relevant exposure signals are assembled into one structured view in seconds, with uncertain or sensitive cases routed to a human.
Valuable risk data stays trapped in documents and external sources
Underwriting often depends on information hidden in uploaded documents, broker submissions, historical notes, public sources, and legacy systems. Bernoly uses AI-assisted data extraction and Data Atlas to structure this information into a usable underwriting view. This helps teams include more relevant data in risk evaluation without increasing manual workload, especially where unstructured or previously inaccessible information would otherwise be ignored.
What Bernoly Helps You Improve
Faster speed to quote
Reduce the time spent gathering, checking, and validating routine risk information before premium generation.
Better risk selection
Use structured internal, external, and contextual data to identify the risks that should move forward, be referred, or require deeper review.
More consistent rating quality
Apply underwriting and pre-pricing logic more consistently across products, channels, regions, and teams.
Lower premium leakage
Reduce missed risk factors, incomplete checks, and inconsistent manual evaluations that can weaken pricing accuracy.
Clearer explainability
Give teams structured reasoning, confidence scores, data sources, and review paths behind AI-assisted evaluations.
Stronger portfolio control
Track underwriting performance across segments, territories, channels, and risk classes to identify where rules, pricing, or referral logic need improvement.
Designed for insurance distribution teams
Insurers
Standardize underwriting across products and regions.
MGAs
Scale underwriting without increasing headcount.
Brokers
Accelerate quoting with better risk insights.
