Own the source code. Deploy autonomous agents for underwriting, claims, and compliance — on your infrastructure, with zero vendor dependency.
ibl.ai is a production-grade AI platform already serving 1.6M+ users across 400+ organizations — delivered as full source code, not a SaaS subscription. Insurance carriers, MGAs, and brokerages deploy it on their own infrastructure with complete control over data, models, and operations.
Insurance demands more than chatbots. Our autonomous agents reason across policy data, actuarial tables, regulatory filings, and claims histories — executing multi-step workflows without human intervention. From first notice of loss to compliance reporting, agents act, not just answer.
With air-gapped deployment, zero telemetry, and a complete audit trail on every agent action, ibl.ai is built for the regulatory scrutiny and data sensitivity that insurance operations require. You own the platform. If you never call us again, it keeps running.
1.6M+ users across 400+ organizations including NVIDIA, Kaplan, and Syracuse University. This is not a pilot framework — it is a hardened platform operating in demanding enterprise environments.
You receive the complete codebase at delivery. Audit every line, modify any component, and extend the platform without asking permission. No black boxes in a regulated industry.
Run on your private cloud, on-premises data center, or air-gapped environment. Zero external dependencies means policyholder data never leaves your perimeter.
Swap or combine Claude, GPT-4, Gemini, Llama, Mistral, or your own fine-tuned actuarial models. No lock-in to a single LLM vendor as the market evolves.
The platform runs independently of ibl.ai after delivery. No usage fees, no API throttling, no forced upgrades. Your operations are never held hostage to a vendor relationship.
Every capability is accessible via RESTful APIs. Model Context Protocol connects agents to policy systems, claims databases, regulatory feeds, and third-party data sources natively.
Agents ingest application data, pull third-party risk signals, cross-reference actuarial tables, and generate a scored underwriting recommendation with supporting rationale — without underwriter intervention on standard risks.
On first notice of loss, agents classify claim type, verify coverage against policy terms, flag fraud indicators using historical patterns, initiate adjuster assignment, and draft the acknowledgment letter — all autonomously.
Agents continuously monitor state DOI bulletins, NAIC model law updates, and internal policy filings — flagging gaps, generating compliance summaries, and routing action items to the appropriate compliance officers.
MentorAI agents deliver personalized pre-licensing education, state-specific exam prep, and continuing education to insurance agents — adapting content based on performance gaps and jurisdiction requirements.
Agents analyze renewal books, identify lapse risk using behavioral and claims signals, generate personalized retention offers, and trigger outreach workflows — coordinating across CRM, billing, and policy systems.
Agents query loss run databases, execute statistical analyses, detect emerging loss trends, and produce draft actuarial memos — enabling actuaries to focus on judgment rather than data assembly.
Traditional chatbots answer questions. Autonomous AI agents take action, reason over context, and deliver measurable outcomes.
ibl.ai deploys autonomous AI agents that go beyond simple Q&A. Our agents reason, plan, and execute multi-step workflows while you retain full code ownership and infrastructure control.
The entire platform — models, agents, data pipelines, and APIs — runs inside your network perimeter. Policyholder PII, claims data, and actuarial models never traverse the public internet or reach ibl.ai infrastructure.
No usage data, no model inputs, no agent outputs, and no behavioral analytics are transmitted to ibl.ai or any third party. What happens in your environment stays in your environment — by architecture, not by policy.
Every agent action — every API call, database query, decision branch, and output — is logged with timestamp, user context, and model version. Audit logs are structured for DOI examination, SOX review, and E&O litigation support.
Multi-tenant architecture with granular RBAC ensures underwriters see underwriting data, claims adjusters see claims data, and compliance officers see compliance workflows — with no cross-contamination between lines of business or departments.
Run open-source or fine-tuned models entirely on your hardware. No policyholder data is sent to OpenAI, Anthropic, Google, or any external model provider unless you explicitly configure and approve that routing.
Because you own the full source code, your security team can audit every component — agent logic, data connectors, API handlers, and authentication flows — before deployment and after any update.
Your security, compliance, and engineering teams review the complete codebase before a single agent touches production data. No trust-me-it's-secure black boxes in a regulated environment.
Add a new claims workflow, integrate a proprietary actuarial model, or extend the credentialing system — without filing a feature request or waiting on a vendor roadmap. You control the product.
Run on AWS GovCloud, Azure, Google Cloud, your own data center, or a fully air-gapped environment. The source code is infrastructure-agnostic and carries no deployment restrictions.
If ibl.ai ceased to exist tomorrow, your platform keeps running. No license keys, no API tokens, no SaaS endpoints to expire. Your operations are not contingent on our business continuity.
Your engineering team inherits a production-grade AI platform they can understand, extend, and own. Over time, you build institutional knowledge rather than institutional dependency.
ibl.ai delivers the complete source code of the AI platform to your environment. Your team installs it on your chosen infrastructure — cloud, on-premises, or air-gapped. Full documentation, architecture diagrams, and deployment runbooks are included.
ibl.ai engineers work alongside your team to configure agents for your specific workflows — underwriting rules, claims triage logic, compliance monitoring feeds, and agent training curricula. Integrations with your policy administration, claims, and CRM systems are built and tested together.
You go live on your timeline, on your infrastructure, under your control. ibl.ai provides knowledge transfer so your team owns operations fully. Ongoing support is available, but the platform runs independently — no dependency on ibl.ai for uptime, performance, or continuity.
Percentage of standard-risk submissions processed end-to-end by autonomous agents without underwriter intervention, freeing senior underwriters for complex and specialty risks.
Reduction in per-claim handling costs on eligible claim types through autonomous triage, coverage verification, fraud flagging, and adjuster coordination — without adding headcount.
Estimated annual savings for mid-to-large carriers from automated regulatory monitoring, filing gap analysis, and compliance reporting — replacing manual review cycles across multiple state jurisdictions.
Reduction in time-to-productive for new insurance agents using MentorAI for personalized pre-licensing prep, product training, and state-specific compliance education.
Percentage point improvement in renewal retention rates when AI agents identify at-risk policies and trigger personalized retention workflows 60-90 days before expiration.
Insurance is regulated at the state level across 50+ jurisdictions. Carriers must demonstrate that AI-assisted underwriting and claims decisions are explainable, non-discriminatory, and consistent with filed rates and forms.
Every agent decision is logged with full reasoning chains, data inputs, and model versions. Audit trails are structured for DOI examination requests. Air-gapped deployment ensures no policyholder data leaves your jurisdiction's infrastructure.
The NAIC's model bulletin on AI systems requires insurers to govern AI use in underwriting and claims, ensure human oversight, and document bias testing and model validation processes.
Source code ownership enables full model documentation and validation. RBAC enforces human-in-the-loop controls where required. Bias detection hooks can be integrated into underwriting agent workflows with logged outputs for governance reporting.
The Gramm-Leach-Bliley Act and state privacy statutes require carriers to protect nonpublic personal financial information and restrict its use and disclosure.
Air-gapped deployment and zero telemetry ensure NPI never leaves your perimeter. Role-based access controls enforce data minimization. No third-party LLM API calls means policyholder data is never processed outside your environment.
Publicly traded insurance holding companies must maintain auditable internal controls over financial reporting, including AI systems that influence reserve calculations or financial disclosures.
Complete agent audit trails with immutable logs support SOX control documentation. Source code auditability allows internal audit teams to verify agent logic. Model versioning ensures reproducibility of any AI-assisted financial analysis.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.