Solutions · Insurers

AI built for insurance carriers

NextRev finishes the call after the call. The policy-admin update, the coverage record, and the claims and service notes draft themselves from what was said, so your licensed reps stay with the policyholder instead of re-keying systems, and nothing posts until a rep approves it.

The hidden cost on every carrier floor

Across sales, service, and claims, your reps end every call by leaving the conversation to feed the systems of record:

  • Documenting the call into the policy administration system
  • Updating coverage, endorsement, and beneficiary records after a change
  • Writing claims notes and service notes for the file
  • Logging the required compliance and disclosure language
  • Re-keying the same details across the dialer, policy admin, and CRM
  • Building QA scorecards and the end-of-day reporting

Industry benchmarks put after-call work at roughly 6 to 12% of an agent's paid time, and compliance-heavy lines like insurance likely sit at the high end (our read). Hand-keyed records are not free either: studies of manual data entry report errors of roughly 0.3% to 6.6% depending on the method. Those are sourced figures, not our claim. We size yours from your own calls.

What NextRev does for your carrier floor

It runs on the dialer, policy-admin system, and CRM you already have. No rip-and-replace.

A live cheat sheet, during the call

Captured, missing, and unsure fields on screen as your rep talks, so they stay with the policyholder instead of hunting through the policy record.

The work, already done, after the call

The summary, the policy-admin update, and the claims or service note draft themselves and queue for one-click approval. Your rep reviews; nothing posts on its own.

Works on the systems you already have

Whether your dialer hands over live audio or just the recording, the value never depends on the hardest piece to connect across the policy-admin and CRM stack.

Provenance, not generation

Every field traces to a moment in the call. Anything unsure is flagged, never invented. Your rep's approval is the only point a regulated record moves.

Why insurance carriers are moving now

The technology to do this safely has arrived, and carrier adoption is following.

9 in 10

About 90% of insurers report being somewhere on the generative-AI journey, with roughly 55% already in early or full deployment, the strongest uptick of any AI technology surveyed.

Conning, 2025 AI in Insurance survey
Top AI use

Insurers are now about as likely as technology firms to report using AI, and contact-center automation and capturing information sit among the most common uses organizations name.

McKinsey, State of AI 2025
Governed, not guessed

Two dozen states have adopted the NAIC model bulletin asking insurers for a documented, monitored AI program. NextRev fits that posture: it writes only what traces to the call, and only after a rep approves.

NAIC Model Bulletin on AI

See it on your own calls

We start with a paid diagnosis sized on your carrier's own numbers. You leave with a plan whether or not we build.

Request a Demo

Sources

  1. Conning, "2025 AI in Insurance: The C-Suite Verdict." About 90% of insurers on the generative-AI journey, ~55% in early or full deployment. conning.com
  2. McKinsey, "The state of AI in 2025." Insurers as likely as tech firms to use AI; contact-center automation and information capture among top uses. mckinsey.com
  3. McKinsey, "The future of AI for the insurance industry." AI across underwriting, claims, and customer-service operations. mckinsey.com
  4. NAIC, "Model Bulletin: Use of Artificial Intelligence Systems by Insurers." Calls for a written, validated, monitored AI program. naic.org
  5. Locke Lord / InsureReinsure, "Wisconsin Becomes the 24th State to Adopt the NAIC Model Bulletin." State adoption count as of March 2025. insurereinsure.com
  6. Voiso, "Average After-Call Work Time." After-call work as 6 to 12% of agent time. voiso.com
  7. Garza et al., "Error Rates of Data Processing Methods in Clinical Research" (systematic review, NCBI/PMC). Manual data-entry error roughly 0.3% (keyed) to 6.6% (record abstraction). ncbi.nlm.nih.gov