Insurance analytics software for SaaS platforms.

Insurance analytics software turns policy, premium, claims and exposure data into dashboards for metrics such as loss ratio, combined ratio, gross written premium and claims cycle time. Carriers often get it inside their core policy and claims systems, while MGA, claims and policyholder-portal software vendors embed it so their own customers see those metrics in the product. Databrain is built for that second case: white-labeled, multi-tenant dashboards on the database your platform already uses.

Databrain scopes every embedded dashboard to one tenant through a server-issued guest token that carries the customer's ID, and injects that tenant filter into the SQL it generates for builder metrics, so a policyholder sees only their policies and a broker only their book. Custom SQL metrics must reference the tenant variable explicitly. BerryBox uses this so every broker on its platform sees only their own portfolios, without duplicate dashboards per broker.

Insurtech platforms usually show loss ratio and combined ratio by line, gross written and earned premium, claims volume and cycle time, open claims by stage and exposure trends, and benefits platforms add enrollment and utilization by employer group. In Databrain your team defines each metric once against your own policy and claims schema using the Visual Builder, Custom SQL or Chat Mode, then reuses it across every customer's dashboard.

Yes, Databrain lets brokers and underwriters ask questions in plain English when you enable Chat Mode in the embed. A broker types something like "loss ratio by line for the last six months", Databrain writes SQL against your modeled insurance data, runs it with the same tenant filter as dashboards on table- and database-based tenancy, and returns a chart. You bring your own LLM key from OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Llama or Mixtral.

For policyholder data, Databrain offers SOC 2 Type 2 and GDPR compliance, a completed VAPT, and ISO 27001 work that is still in progress, with current status on trust.usedatabrain.com. Policy and claims tables are read over a read-only connection rather than copied, cached results expire on a time-to-live you set, and a server-issued guest token ties each embedded dashboard to a single tenant, such as one brokerage. Self-hosting, VPC peering and SAML SSO come with the Enterprise plan; BerryBox deployed Databrain on Amazon EKS.

Databrain is built for customer-facing embedding, while Power BI is built first for internal reporting. BerryBox tried embedding Power BI into its HR and broker SaaS app and, per its published case study, hit brittle ETL, a DAX learning curve and risky deployments. After moving to Databrain, its analysts write standard SQL, every broker sees only their own portfolios, and the rollout took three weeks instead of an estimated three months.

Most teams put their first customer-facing Databrain dashboard live in 2 to 4 weeks. BerryBox, an insurtech serving HR teams and insurance brokers, went live in three weeks, and its published case study puts the savings at $250K and 6 months of engineering effort. The steps are connecting your database, setting the tenant model, building dashboards in the builder, and adding the dbn-dashboard web component to your app.