Autonomous AI discovery built for insurance.
Continuous AI discovery across your policy, claims, channel, and reserves data. Every finding ships with the underlying queries, the cohort definition, and the cost of the run. The cohort-level book questions that never make the actuarial deep-dive queue, answered before your next book review.
Built for Chief Underwriting Officers, Heads of Claims, Chief Actuaries, and Heads of Pricing at P&C, life, and health carriers.
The cohort-level book questions that never make the actuarial deep-dive queue, running overnight against your data.
Your team owns the rate filings, the reserves, the regulatory posture. The agent runs unattended against your policy, claims, channel, and reserves data — and ships findings into a queue your team triages. Every claim carries the underlying queries and the data-validated number. The agent reads where your data lives; it never writes back, never bypasses RBAC, and logs every step for the rate-filing record.
See how the agent reads cohort frequency and severityThe combined ratio looks fine. The sub-line underneath is where the book actually moved.
Three benchmarks every underwriting, claims, and actuarial leader recognizes. Each points to the same gap: your data already holds the signals — frequency drift, channel-quality drag, sub-line economics — but they only surface when somebody asks the right question. Most weeks, nobody does.
Three findings the agent surfaced, paired with the recommendation it shipped.
Three shapes the book sees every quarter: a single-line frequency drift the aggregate concealed, a cross-system channel-quality pattern the underwriting score didn't catch, and a sub-line economics shift the combined ratio averaged away. Same auditable trail every time — severity, the underlying queries, the cohort definition, the cost of the run.
Point the agent at your book of business for a week. Triage what it finds Monday morning.
Five surfaces the team already owns, one continuous discovery.
Same engine, same data, five categories of work your team is already doing — or wants to do, if the queue ever clears. Pick the surface that matters this quarter.
The cohort whose frequency moved, and the aggregate that hid it.
Frequency and severity drift inside cohorts the dashboard reports in aggregate. The agent reads claims.fnol_daily, policies.in_force, and cohort definitions (age band, vehicle class, peril, geography) together — and surfaces the segments where frequency or severity is moving before the next reserve review catches it. The deep dive lives in how the agent decides what to investigate.
- Cohort-level frequency drift across age band, vehicle class, geography, and peril
- Severity development by accident-year cohort
- Catastrophe vs. attritional loss decomposition
- Frequency masking inside aggregate-flat lines
- Loss-cause clustering and emerging-risk detection
Built for carriers who want an analyst running cohort math overnight, and a record the rate filing can rely on.
Three things every finding from DecisionBox carries. None of them changes how your team works in your policy admin system, your claims platform, or whatever data tools and BI you already standardized on.
Sees what nobody had time to investigate
The agent picks which cohorts, channels, sub-lines, and reserve segments to investigate from your policy, claims, and reserves data. Findings ship into a queue your team triages in the morning. The cohort-level frequency drift stops sitting in the data waiting for the next reserve cycle.
Every number you'd defend at the book review
The underlying queries the agent ran, the cohort definition, the data window, the cost of the run, the validation checks. Your team re-runs anything, extends anything, overrides anything. No black boxes, no numbers the actuarial review can't reproduce, no "trust the AI."
Reads your data where it lives. Filing-ready record.
Self-host on Docker, Helm, or Terraform — or managed cloud on your terms. The agent reads your data where it lives, never writes back, never bypasses RBAC, and logs every query and reasoning step for the rate filing, the actuarial review, and the audit. Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Open source, AGPL v3. Built for the regulator, the model validation review, and the data-residency posture you already enforce.