Every claims file an adjuster pastes into a cloud model is policyholder data handed to a third party, and your state DOI still holds you answerable for it. We'll build the whole system on your own hardware instead: the agents, the orchestration, and the models that carry your carrier's real work, and policyholder data never leaves your network.
Every deployment starts on-site, with the people who do the work.
Insurers process some of the most sensitive personal data in any industry. Air-gapped AI claims processing eliminates the risk of cloud AI transmitting that data outside institutional control.
Insurance carriers collect and process an extraordinary breadth of sensitive personal data: health records for life and health insurers, financial information for all lines, driving histories for auto, property details for homeowners, and claims histories that paint intimate portraits of policyholders' lives. When an adjuster pastes a claims file into a cloud AI service, all of that information travels to infrastructure controlled by a third party. Insurance is one of eleven regulated industries where this structural conflict between cloud AI and data confidentiality is most acute.
For health insurers, HIPAA establishes strict requirements for protected health information (PHI) - the same framework governing medical practices. The Privacy Rule limits disclosure of PHI, and the Security Rule mandates administrative, physical, and technical safeguards. Cloud AI processing of PHI requires a Business Associate Agreement (BAA) - but even with a BAA, the structural reality remains: PHI has left your network and sits on someone else's servers. Local AI for life insurance and health plan operations eliminates this transmission entirely.
The NAIC Insurance Data Security Model Law (#668), adopted in whole or part by over 20 states, requires insurers to maintain comprehensive information security programs. State insurance departments enforce data protection requirements that vary by jurisdiction but share a common theme: insurers must demonstrate control over the infrastructure handling policyholder data. Insurance AI without cloud dependency resolves this demonstration entirely - the data never leaves infrastructure you control.
Air-gapped inference insurance carriers can trust - no external connections, no vendor dependency, no data leaving your building.
"No data leaves your building" is a description of network architecture, not marketing language.
Policyholder data never leaves your network. Prompts travel from workstation to server over internal network only. No internet connection required for inference. Data sovereignty insurance carriers demand - achieved through architecture, not contracts.
A physical server in your data center, running on your power, connected to your network, not a hosted service wearing a "local" label. You own it outright. No vendor dependency.
Air-gap GPU server insurance configuration with complete network isolation available after initial setup. For carriers handling the most sensitive health and financial data, this means zero external connections of any kind.
A general model knows insurance the way a textbook does, and nothing about the book you write. It's never opened your policy forms, doesn't know your claims playbook, can't tell your rating rules from the carrier's down the street. We build for the book you run.
Island Mountain builds the agents and the orchestration that carry the work your carrier runs on: claims review and summarization, underwriting support, policy and endorsement drafting, complaint handling, and the regulatory filings your examiners expect on time. It runs on current open-weight models like Kimi K3, ZAI GLM 5.2, Qwen 3.8+, and Nous Hermes 4, served through Ollama or vLLM behind an OpenWebUI front end, or whatever open weights fit the task. All of it governed by our own agentic orchestration and the Woven Security Fabric, so every action carries an identity, an approval, and a receipt, and the credentials expire when the task is done. Policyholder data never leaves your network to make any of it happen.
When the work needs a model that speaks your book, we'll build one. Reasoning models and small language models from scratch, trained to orbit your carrier's ontology: your lines of business, your policy language, your claims process. It learns your terminology and your cadence, not a generic carrier's.
It air-gaps for the lines that carry the most sensitive health and financial data, and reaches the cloud only when a task genuinely needs it. That's your call, workflow by workflow.
Then we hand it back. On-site onboarding, SOPs written for your staff, the keys dropped into your team's hands so they run and rework the workflows without calling us. Want the upkeep off your IT group's plate? We offer tiered support sized to your carrier, and we plug in wherever that's easiest. This is your carrier's legacy. We build it alongside you, and we won't call it done until your people can run it without us.
These aren't chat prompts. They're agentic workflows the orchestration runs start to finish, each step carrying its own identity and receipt, and no policyholder data leaves your network.
Run an on-prem LLM for claims processing: analyze claim submissions, extract key data points, flag inconsistencies, and accelerate adjudication. Process medical records, police reports, and supporting documentation without exposing claimant data to cloud services.
Process applications, evaluate risk factors, and generate underwriting summaries from complex multi-document submissions. Analyze medical histories, financial records, and risk assessments entirely on-premises.
On-prem AI fraud detection insurance workflows: analyze claim patterns, identify anomalies, cross-reference historical data to flag potential fraud for investigation. Process sensitive claimant information without cloud API exposure.
Local AI for policy review: compare policy language across versions, identify coverage gaps, and extract key terms for compliance review. Process entire policy portfolios locally.
Local AI for actuarial analysis: summarize actuarial reports, process loss data, and assist with reserve analysis documentation. The AI assists with documentation and analysis - it does not replace actuarial modeling software.
Draft policyholder communications, denial explanations, renewal notices, and regulatory-compliant disclosures. Maintain consistent language across all customer touchpoints.
Kimi K3, ZAI GLM 5.2, Qwen 3.8+, Nous Hermes 4, and whatever's strongest the month we deploy, served through Ollama or vLLM behind OpenWebUI. Open weights turn over fast, so the honest answer turns over with them. We run the model that fits the claim or the filing in front of it, not the one that topped a leaderboard last quarter.
When nothing off the shelf speaks your book, we build it. Reasoning models and small language models from scratch, trained on your ontology: your lines of business, your policy language, the way your claims move. Small, sharp, and yours alone.
Every model runs under our agentic orchestration and the Woven Security Fabric. Scoped access, ephemeral credentials, a receipt for every action. Air-gapped when the line demands it, cloud-connected when a task earns it.
The cloud costs every month and transmits policyholder data every session. The hardware costs once and keeps everything in-house.
| Cloud AI | Island Mountain build | |
|---|---|---|
| Year 1 Cost | $18,000 - $72,000 (30 users) | One-time purchase (quoted to the deployment) |
| Year 3 Cumulative | $54,000 - $216,000 | Electricity only (~$1,200 - $2,400/yr) |
| Year 5 Cumulative | $90,000 - $360,000 | Electricity only |
| Policyholder Data Location | Cloud provider servers | Your data center. Period. |
| Compliance Risk | PHI/PII transmitted to third party | Zero transmission. Zero risk. |
| Per-Token Fees | $15 - $60 per million tokens | None. Unlimited use. |
| Model Control | Provider decides models and updates | You choose which models to run |
| Claims System Integration | Some platforms offer integrations | Not included. General-purpose AI. |
| Vendor Lock-In | Complete | None. Permissive open-source licenses. |
Knowing the boundaries matters more than knowing the features.
The models are general-purpose large language models, not insurance-specific AI. They're strong at reasoning, analysis, and prose generation, but they're general tools rather than an insurance-specific AI platform, with no fine-tuning on insurance datasets, actuarial tables, or claims databases. What the system does provide is secure AI for customer PII: all policyholder data stays on your hardware.
Island Mountain hardware does not connect to Guidewire, Duck Creek, Majesco, or other claims management platforms out of the box. The AI runs through OpenWebUI - a browser-based chat interface. Moving data between your claims systems and the AI is a manual process.
The system assists with actuarial documentation and report summarization, but it is not an actuarial modeling platform. It does not replace Reserve Pro, Arius, or other actuarial software. AI assists with the prose around the numbers - not the numbers themselves.
After the 30-day included support period, your organization is responsible for OS security updates, model updates, and general system maintenance. This is the same maintenance profile as any Linux server in a professional environment.
HIPAA compliant AI for insurers starts with keeping PHI off third-party infrastructure entirely.
For health insurers, HIPAA's Privacy Rule (45 CFR Parts 160 and 164) limits the use and disclosure of protected health information. The Security Rule mandates administrative, physical, and technical safeguards - including access controls, audit controls, and transmission security. When PHI is processed through cloud AI infrastructure, the cloud provider becomes a business associate requiring a BAA and compliance documentation.
The NAIC Insurance Data Security Model Law (#668) requires insurers to conduct risk assessments, implement information security programs, and maintain oversight of third-party service providers handling policyholder data. Over 20 states have adopted versions of this model law, creating a patchwork of NAIC model law AI compliance requirements that all share a common theme: institutional control over data handling infrastructure.
On-premise AI for P&C insurers addresses this structural gap: these carriers face state regulations governing the protection of personal financial information under GLBA - a framework shared with banks and credit unions (though insurance is carved out from federal enforcement and subject to state implementation). The recurring requirement across all frameworks: insurers must demonstrate adequate safeguards over the infrastructure processing policyholder data. Local deployment satisfies this requirement structurally - the data never leaves your controlled environment.
Disclaimer: This section describes the general regulatory environment regarding AI and insurance data protection. It is not legal or compliance advice. Consult qualified counsel or your state insurance department for guidance specific to your charter type, lines of business, and operational jurisdictions.
Power & Installation: All Island Mountain systems require a dedicated 208V/30A power circuit (NEMA L6-30R). This is standard in server rooms and data closets. Most insurance carriers with an existing server closet already have this infrastructure or can add it for $500-$2,000 through a licensed electrician. The system fits in a standard 4U rack space. Average power draw under typical inference loads is 1.5-2.5 kW.
Yes. Transmitting protected health information (PHI) to a cloud AI provider constitutes disclosure to a third party, triggering Business Associate Agreement (BAA) requirements under HIPAA's Security Rule. Even with a BAA, PHI leaves your network and resides on shared commercial infrastructure outside your direct security controls. On-premises AI hardware from Island Mountain eliminates the business associate dependency entirely.
Island Mountain builds workflows for claims review and summarization, underwriting support, policy and endorsement drafting, complaint handling, regulatory filings, and customer correspondence. The system runs current open-weight models, and custom models we build for your book where the work needs one.
Cloud AI platforms for insurance typically cost $50 to $200 per user per month. For 30 users, that totals $18,000 to $72,000 per year. Over three years: $54,000 to $216,000 cumulative with ongoing data exposure. An Island Mountain build is a one-time purchase, sized and quoted to your workload. Cost parity typically reached within year one to two.
No. The system is configured and air-gapped before it lands through a web browser. Setup requires racking the server, connecting power and network, and opening a browser. 30 days of hands-on support are included. Standard Linux server administration for ongoing maintenance.
Island Mountain is an engineering practice, not a compliance authority. References to HIPAA, NAIC model laws, state insurance regulations, or related data protection frameworks on this page reflect factual descriptions of data handling mechanics - not legal, regulatory, or compliance advice. Consult qualified counsel for compliance determinations specific to your organization and jurisdiction.
Related: Does Running AI Locally Comply with HIPAA?
P&C carrier processing 2,000 claims per month. Every claimant's personal data stays on our servers. State regulators have zero concerns about our AI data handling.
Scenario: P&C CarrierHealth insurer serving 150,000 members. PHI never touches a cloud API. No BAA complexity, no third-party risk, no HIPAA exposure from AI processing.
Scenario: Health InsurerIndependent agency handling multi-line policies across 12 states. Client data sovereignty across every jurisdiction. One hardware purchase solved it.
Scenario: Independent Insurance AgencyOne conversation. No sales pitch. Tell us about your carrier's AI needs and we will spec the right system.
Or call directly: 1-341-441-8740
See all eleven industries we serve or explore: Medical Practices · Financial Services