Insurance Software Development for Faster, Trusted Service

• Claims teams manually collect documents, evidence, policy context, and case history before adjusters can review a claim.
• Each claim can become a slow manual preparation process before investigation begins.
• Adjuster capacity is consumed by preparing cases rather than reviewing them.
• Claims preparation creates unnecessary operational workload across selected claim types.

In 90 days, Computools builds an AI Claims Handling Acceleration solution that structures incoming claims, gathers evidence and policy context, and prepares selected claim types for adjuster review before manual investigation begins. The solution focuses on accelerating claims preparation and handling rather than attempting to automate the entire claim resolution lifecycle.

Target outcomes:

• 30–40% less manual claims preparation and handling work for selected claim types.
• Claims are structured and prepared for adjuster review earlier.
• Faster transition from claim intake to adjuster review.

• Service teams handle high volumes of repetitive policyholder requests.
• Routine inquiries often require agents to manually check policy, claim, or document status.
• Growing policyholder request volumes create pressure to scale service headcount.
• Contact centers become operational bottlenecks as servicing demand grows.

In 90 days Computools builds an AI Policyholder Service Automation layer connected directly to policy and claims data. The solution handles selected routine policy servicing, claims-status, and document requests while escalating sensitive or complex cases to human agents through defined workflow and escalation logic.

Target outcomes:

• Automate up to 50–70% of selected routine service requests.
• Reduce repetitive servicing workload for human agents.
• Scale policyholder service without proportional contact-center headcount growth.

• Agents and brokers spend too much time preparing quotes.
• Producer teams manually research customers and collect context before engaging opportunities.
• Follow-up planning adds repetitive preparation work across broker- and agency-led distribution.
• Distribution productivity is limited by administrative work surrounding each opportunity.

In 90 days Computools builds an AI Agent & Broker Productivity Intelligence solution that prepares customer context, quote information, and next-best actions before agents and brokers engage opportunities. The solution is designed for broker-led, agency-led, MGA, commercial insurance, and life/annuities distribution models where producer preparation creates meaningful operational workload.

Target outcomes:

• 20–30% less quote preparation, customer research, and follow-up workload.
• Producer teams receive prepared context before engaging opportunities.
• Agents and brokers can handle more opportunities without proportional growth in distribution cost.

• Legacy policy administration systems slow product changes.
• Existing insurance cores create friction around integrations, servicing, claims workflows, and digital transformation.
• Full core replacement introduces significant transformation risk.
• Insurers need modernization progress without committing immediately to a multi-year replacement program.

In 90–180 days, Computools launches the first Policy Administration Modernization Layer around a selected policy administration or claims workflow. Using APIs, workflow components, and integration services, the modernization layer improves one priority workflow while keeping the existing insurance core intact.

Target outcomes:

• Faster delivery of selected priority workflows.
• Reduced integration friction around legacy insurance systems.
• A scalable proof point for broader modernization without replacing the entire insurance core.

• Fraud investigators spend too much time collecting context before evaluating suspicious cases.
• Claim history, policyholder information, behavioral signals, and suspicious patterns must be assembled before investigation can begin.
• Investigation capacity is consumed by preparation rather than judgment.
• High investigation volumes increase manual workload for SIU and fraud teams.

In 90–120 days, Computools builds an AI Fraud Investigation Intelligence solution that assembles claim history, policyholder data, behavioral signals, and suspicious patterns before investigator review. AI prepares fraud cases for human investigation rather than autonomously determining whether fraud has occurred.

Target outcomes:

• 40–50% less manual fraud investigation preparation time.
• Fraud cases open with relevant context and suspicious signals already assembled.
• Investigators spend more time on judgment and less time preparing cases.

• Compliance and audit teams repeatedly collect regulatory evidence across review cycles.
• Teams manually reconcile data and track control updates.
• Audit, regulatory examination, and AI governance requirements create significant documentation workload.
• Preparation work intensifies before every regulatory or audit review.

In 90–120 days, Computools builds an AI-Assisted Regulatory & Audit Evidence Automation layer that collects evidence, reconciles data, tracks control updates, and prepares review-ready documentation for audits, regulatory examinations, and AI governance reviews. The solution organizes evidence, documentation, reconciliation, and control updates into a controlled review workflow.

Target outcomes:

• 30–50% less regulatory and audit evidence preparation effort for selected workflows.
• Review-ready evidence and documentation before audits and regulatory examinations.
• Less repetitive rebuilding of regulatory and audit evidence across review cycles.