LLM.co · Private AI & Custom AI DevelopmentPrivate AI for insurance

AI for insurance claims that keeps policyholder data in your environment

A claim file is long and mixed. It holds the FNOL report, photos, adjuster notes, repair estimates, medical bills, police reports, recorded statements and letters from counsel. Adjusters spend a large share of their day reading and rekeying that material. Well-built AI for insurance claims reads the whole file, fills the fields and drafts the summary, so the adjuster starts from a complete picture.

That file is also full of personal, health and financial information. State insurance data security laws, privacy rules and your own reinsurance and vendor agreements limit where it can go. LLM.co builds private AI for insurers on open-weight models that run in your data center or your own cloud account. Claims and underwriting data never pass through a third-party model API.

Claims automation workflows that hold up in production

Claims automation works best where volume is high and the steps are already written down in your claims manual.

• FNOL intake. Emails, web forms, call transcripts and attachments are read, classified by line and loss type, and set up in the claims system with fields filled and confidence scores shown.
• Document indexing. Incoming mail and attachments are labeled and filed to the right claim and exposure.
• Triage. Each new claim is scored for complexity and routed, with fraud indicators flagged for SIU review and the reason stated.
• File summaries. A running summary of the claim is kept current for supervisors, reserving discussions and handoffs.
• Correspondence drafts. Acknowledgment, status and document request letters are drafted from your approved templates for adjuster review.

Underwriting AI for submissions and renewals

Commercial submissions arrive as broker emails with ACORD forms, schedules of values, loss runs and supplemental applications. Underwriting AI extracts that data into your rating and workbench tools, compares the risk against your guidelines and appetite, and drafts the underwriting memo with each point tied to its source page.

Renewals follow the same pattern. The system pulls the prior year's file, notes what changed and lists open questions for the underwriter. The underwriter makes every pricing and acceptance decision.

Guardrails insurers should require

Claims and underwriting decisions affect policyholders directly, so the controls matter as much as the model. Every system we build for insurers includes these.

• No automated denials or coverage decisions. The model drafts and flags. A licensed person decides.
• Citations to the policy form, endorsement or document page behind each statement.
• Role-based access mirrored from your claims and policy systems.
• Full audit logs of inputs, retrievals and outputs, kept under your retention schedule.
• Documented model inputs so your team can review triage and fraud flags for unfair discrimination concerns.

What to pilot first

AI for insurance claims is easiest to evaluate on intake, where every field has a known correct value. Pick one high-volume intake queue in one line of business, such as personal auto glass or property FNOL. You already have years of closed claims there to build an evaluation set from. Avoid starting with litigated or catastrophe files, where documents are least consistent and the cost of error is highest.

Work starts with a two-week discovery sprint. A focused first system usually reaches production in eight to twelve weeks, connected to Guidewire, Duck Creek or your own platform through existing APIs.

Why insurers choose custom AI development

Packaged claims tools are built for an average carrier. Your claims manual, coverage forms, authority levels and reserving practices are your own. Custom AI development from LLM.co builds around them, and you own the code, prompts, evaluation sets and any fine-tuned weights at handover. There is no per-claim or per-seat fee. Your team can run the system, or we can operate it under a support agreement with quarterly evaluation reports.