Insurance & Public Adjusters AI Solutions

We design for adjuster review on high-impact or low-confidence outcomes: thresholds, sampling, and escalation paths are configurable. Every model-assisted field stores provenance, version, and reviewer identity for audit.

Yes. We use APIs, events, and batch patterns depending on latency needs—claims creation, exposure updates, document attach, and financials. We have experience with major suites and bespoke mainframe bridges.

We apply minimization, encryption, retention policies, and regional processing where required. Role-based access and data masking are standard; DPIAs and vendor controls align to your security team’s checklist.

No—the goal is to remove repetitive normalization and search so adjusters focus on judgment, negotiation, and customer care. Public adjusters gain faster package assembly without sacrificing professional oversight.

Focused FNOL or document pilots often show measurable cycle reduction in 10–14 weeks. SIU and graph analytics depend on data history but typically produce referral-quality gains within two quarters.

Yes—queue prioritization, geographic triage, and contractor/vendor deduplication patterns are common extensions. We scale ingestion and batch scoring with your cloud footprint.