Submission triage. Underwriting copilots. Claims straight-through processing. Subrogation recovery. Fraud detection. Built carrier-grade — MRM, audit trails, and regulator-readable architecture from day one.
Each is a system we've shipped or have direct shipped credibility for. We don't take engagements outside this surface area unless we can name an adjacent build we've already done.
Multi-agent submission digitization — broker email, ACORD, schedules, loss runs. Risk insight surfacing via RAG pipelines. Carrier-grade MRM and human-in-the-loop scaffolding throughout.
PRIMARY KPI · Submission-to-quote · Hit rate
Underwriter workbenches with RAG-based policy and appetite retrieval, risk insight summarization, and pricing override audit trails. Built for desks that move billions in premium.
PRIMARY KPI · Cycle time · Hit rate
Straight-through processing on low-complexity claims, anomaly detection on suspicious patterns, and adjuster copilots for complex losses. Auditable, explainable, regulator-ready.
PRIMARY KPI · STP rate · Claims leakage
Subrogation opportunity identification across claim narratives and structured data. SIU triage with explainability. Built to survive state regulator scrutiny on AI-assisted denial.
PRIMARY KPI · Subrogation $ recovered · SIU yield
broker email · ACORD · schedules · loss runs
extract & structure
LangGraphretrieval-grounded
vector DBscore & route
underwriter-ready
Representative builds — the technology patterns we bring to insurance. Where we cite results, they’re from work our team has shipped; everything else is what we’d build for you.
INS.T1 · Snowflake
Guidewire/Duck Creek policy and claims data landed in Snowflake (CDA-style ingestion), modeled with dbt into underwriting- and claims-ready marts.
Stack · Snowflake · dbt · Airflow
INS.T2 · LLM + RAG
Multi-agent intake over broker email, ACORD forms, schedules, loss runs; RAG-based appetite and policy retrieval with MRM scaffolding. Our team shipped this pattern inside a venture-backed insurance AI platform.
Stack · Claude/GPT · LangGraph · vector DB · MLflow
INS.T3 · Databricks
Delta Lake claims lakehouse, Spark-scale feature pipelines, anomaly and subrogation-opportunity models with MLflow-governed lifecycle and explainability for SIU and regulator scrutiny.
Stack · Databricks · MLflow · Unity Catalog
INS.T5 · Digital transformation
Assessment and phased migration of on-prem actuarial and claims data estates to a governed cloud platform — the prerequisite 95% of stalled insurance AI programs skipped.
Stack · Snowflake or Databricks · dbt · NIST AI RMF
NAIC model bulletins on AI. IRDAI guidance on use of AI/ML. State AI laws (Colorado, New York DFS). EU AI Act high-risk classification for insurance. NIST AI RMF for model risk management.
We design with audit-readiness as a first-class concern. The architecture diagram is one we'd defend to a state regulator or audit committee, not just one we'd show a buyer.
Our founding team has shipped AI inside venture-backed insurance AI, including multi-agent submission digitization, RAG pipelines for risk insight, and carrier-grade MRM with human-in-the-loop scaffolding.
Statistiq is building a flagship product for insurance — focused on a specific submission, underwriting, or claims workflow where we have direct shipped credibility and where the regulatory frame favors a senior-practitioner partner over a generic SI. We'll share more in 2026.
Most first conversations are 30 minutes with a senior partner. We'll tell you whether it's a fit, whether AI is the right answer, and what we'd build first.