FNOL triage agent
8-min handle time
(was 45)
Categorize and route incoming claims at intake. Extract loss type, severity, coverage applicability, and routing target. Cut handle time 30–60% while improving routing accuracy.
Insurance
AI agents for carriers that need to ship.
AISD builds AI agents and software for P&C and life carriers — FNOL triage, claims document processing, underwriting copilots, customer-service deflection, and fraud scoring. HIPAA-aligned, SOC 2 Type II audit in progress, on-prem deployment available where state regulation requires.
6 proven use cases · 4–9 mo typical payback · On-prem & VPC available
Use cases
Six places AI agents move the needle for carriers.
Claims document processing
30–50%
adjuster time saved
Extract structured data from policy documents, medical records, adjuster notes, and demand letters. Validate against business rules, route exceptions to humans.
Underwriting copilot
2–3×
quote throughput
Surface risk signals, comparable cases, and policy precedents during quote review. Underwriter stays in control; the copilot does the legwork.
Customer-service deflection
25–40%
auto-resolution
Resolve policy lookups, billing questions, and ID-card requests without human handoff. Hand off cleanly when the question requires judgment.
Fraud-signal scoring
False-positive ↓ 38%
vs rule-based
Combine claim narratives with structured signals (claim history, geography, network ties) to score fraud risk. Outputs human-readable rationale, not a black-box score.
Subrogation discovery
5–15× faster
case identification
Read closed claim files to identify subrogation opportunities the team missed. Surface the strongest candidates first, with the supporting facts pulled out.
Compliance & data handling
Built for the constraints insurance actually has.
State DOIs, HIPAA, model risk management — the constraints that make insurance AI different from generic enterprise AI. We build for them, not around them.
Data minimization at the boundary
Agents see only the fields they need. PII and PHI are redacted before they reach the model. Field-level audit logs record what was seen.
On-prem and VPC deployment
Where state regulation or carrier policy requires, we run open-weight models on dedicated infrastructure with no data leaving the customer perimeter.
Decision audit trail
Every model call is logged with inputs, outputs, and decision rationale — searchable and exportable for state DOI exams and internal audits.
Human-in-the-loop on coverage decisions
Coverage and reserve-setting decisions are AI-assisted, not AI-made. The agent surfaces evidence; the adjuster decides.
What an engagement looks like
Most insurance customers start with a 2-week discovery sprint.
We pick a single use case (usually FNOL triage or document processing — high-volume, structured outputs, measurable business metric), scope the architecture against your data security constraints, and build a 1-week throwaway prototype on real anonymized data. Output: a fixed-price proposal for the production build.
Production builds typically run 8–14 weeks. Most customers move on to a managed AI services retainer once the agent is live, while their internal team ramps to take over operations.
Services for insurance
Insurance-aligned case studies
Frequently asked
Common questions.
What AI use cases work for insurance companies today?
Five proven use cases for P&C and life carriers. FNOL (first notice of loss) triage agents — categorize and route incoming claims, cut handle time 30–60%. Claims document processing — extract structured data from policy docs, medical records, and adjuster notes. Underwriting copilots — surface risk signals and policy precedents during quote review. Customer-service deflection — agents that resolve policy and billing questions without escalation. Fraud-signal scoring — combine claim narratives with structured data to flag suspicious claims for manual review.
How does AISD handle PHI / PII / regulatory requirements in insurance?
Three patterns. Data minimization: agents see only the fields they need; everything else is redacted at the boundary. Audit logging: every model call is logged with inputs, outputs, and decision rationale — searchable and exportable for regulatory review. On-prem / VPC deployment: where state regulations or carrier policy requires, we run open-weight models on dedicated infrastructure with no data leaving the customer's perimeter. We deliver HIPAA-aligned engagements; SOC 2 Type II audit is in progress.
What's the typical ROI for an AI agent in insurance?
Outcomes vary by use case. Typical AISD insurance customer outcomes: FNOL triage reduces processing from 45 minutes to 8 minutes per claim (~80% reduction). Document extraction cuts adjuster review time 30–50%. Customer-service deflection resolves 25–40% of inbound queries without human handoff. Payback period: 4–9 months on the build cost. ROI is highest when the workflow has high volume, structured input/output requirements, and a measurable downstream metric (claims-cycle time, NPS, loss ratio).
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How do you evaluate AI agent performance?
Three layers of measurement. Offline: a golden test set of 50–500 representative inputs scored automatically (model-graded) and by humans on a sample. Run on every PR. Online: per-call metrics — latency, cost, tool-call success rate, schema-validation pass rate, downstream business outcome. Human-in-loop: weekly review of escalated and low-confidence cases, fed back into the test set.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
Is AISD SOC 2 / GDPR / HIPAA compliant?
GDPR: yes — we handle EU personal data under standard data-processing agreements and apply data-minimization patterns (redaction at source, retention windows, right-to-erasure tooling). SOC 2: Type II audit in progress. HIPAA: we deliver HIPAA-aligned engagements (BAAs available, PHI handling patterns established) but do not yet hold a third-party HIPAA attestation. We will not claim certifications we don't hold.