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Transforming underwriting, claims, and reinsurance with AI, data, and automation — LOB-specific accelerators and a governed data foundation, built inside your environment by a principal-led squad.
We work across the insurance value chain — from the strategy and architecture decisions through the automation, models, and data foundation that make them real. Expertise spans underwriting, claims, and reinsurance across personal and commercial property/casualty lines.
We assess where AI and automation genuinely move your loss ratio, expense ratio, and cycle times — then sequence a roadmap against it. You get a technology assessment, a prioritized opportunity map, and a business case before anyone writes code.
Target-state architecture for the insurance estate — policy admin, claims, billing, and reinsurance systems, plus the data platform and automation frameworks that sit across them. Designed for the regulatory reality you actually operate in.
Submission to bind, automated. AI-driven risk analysis and prioritization, automated email handling and follow-ups, LOB-specific questionnaire templates, flexible rating models, and endorsement and subjectivity tracking — on our InsightUW accelerator.
Claims triage and low-value process automation, automated coverage discovery, fraud signals, and predictive severity to support reserving decisions. Handles the real inputs — audio, video, images, and documents, not just clean forms.
Treaty management and ceded reinsurance automation, bordereaux processing, analytics for risk transfer strategy, and the reporting and compliance trail that treaty settlements require. Covers assumed as well as ceded.
Extraction from the documents insurance actually runs on — ACORD forms, statements of value, loss runs, broker submissions, policy wordings, and bordereaux. Confidence scoring and human-in-the-loop routing so low-certainty extractions get reviewed rather than trusted.
Integration with policy administration, claims, billing, and rating systems, plus submission clearance and duplicate detection. We build the round-trip — data out, decisions back in — so automation writes to the system of record rather than beside it.
The governed lakehouse underneath everything else — conformed policy, claim, premium, and loss models with the precision an actuarial audit survives. Delivered with our Databricks POD when the platform itself needs building.
Rating models, portfolio benchmarking, loss prediction, churn and retention scoring, and reserve development. Built with MLflow-tracked lineage so every score on an underwriter's screen traces to the model version that produced it.
Copilots for underwriters, claims handlers, and brokers — grounded in your own policy language, guidelines, and submission history. Evaluation harnesses, guardrails, and tracing come standard, because a hallucinated coverage answer is a claim.
Model governance, decision auditability, PII handling, and statutory and regulatory reporting. Built so a regulator asking why a risk was priced or declined a year ago gets a reconstructable answer, not a shrug.
Ongoing operations for the platforms and models we build — monitoring, retraining, new LOB onboarding, and support for teams that want the capability without standing up a dedicated insurance data practice.
Every Insurance POD starts from products we have already built and hardened. They get configured to your lines and your systems rather than written from scratch, which is most of where the timeline comes from.
All-in-one underwriting platform — AI-driven submission to bind, automated data extraction, business rules and workflows, LOB templates, flexible rating, endorsement and subjectivity management, and AI-driven audit and renewal packages.
AI claims accelerator with automated coverage discovery, AI insights and recommendations, and native handling of audio, video, image, and document evidence.
Insured and broker 360 — a comprehensive entity view with attributes, scores, and segmentation, plus the full customer journey. The serving layer for underwriter and account-management screens.
Industry and company intelligence for your insureds and brokers — industry classification and trends, company profiles, credit ratings and financial stability, subsidiaries, M&A activity, governance, and real-time news.
Document extraction built for P&C submission variability — classification, confidence taxonomy, conflict resolution, and clickable citations back to the source page so every extracted value is checkable.
Rating models, portfolio benchmarking, and loss and churn prediction, plus our deployable P&C lakehouse blueprint — 120+ governed tables across nine schemas, adapted rather than rebuilt.
Most engagements start in one pillar and expand once the foundation is proven.
Accelerators ship with templates, questionnaires, and extraction schemas per line, so a new LOB is a configuration exercise rather than a new build.
An Insurance POD is 4–8 senior engineers and insurance specialists working inside your environment, on your backlog, with your underwriters and adjusters in the room.
Current-state review of the chosen LOB — submission flow, systems, document mix, and cycle-time baseline. Output is a target design and a sequence your team has signed off on.
Accelerator stood up in your environment, PAS and claims integrations wired, extraction schemas configured for the line, and the first real submissions flowing end to end.
Workflows, rules, rating, and models delivered in two-week increments against live volume. Accuracy and straight-through-processing rates measured every increment, not at the end.
Runbooks, model monitoring, audit trails, and paired delivery with your team until they own it. You keep the code, the configuration, and the documentation.
Most carriers begin with one line of business and expand once the accelerator is proven in production.
2–3 weeks, 2 principals
10–14 weeks per LOB, 4–8 members
Rolling, scales up and down
Field notes from the underwriting, claims, and data work.