Your rep ends the call, then retypes it into three systems.
NextRev drafts the policy-admin update, the coverage change and the service note from what was said. Your rep reviews and approves. No regulated record posts on its own.
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 numbers come from the cited sources and describe the industry, not a NextRev result. 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 are drafted for your review 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.
Every field shows where it came from
Every field carries the moment it came from, so a reviewer can check it without re-listening to the call. Anything the system is unsure of is left empty and 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. The figures below are industry context, not a NextRev result.
About 90% of insurers report generative-AI work underway, with roughly 55% already in early or full deployment, the strongest uptick of any AI technology surveyed.
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.
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.
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.
Sources
- Conning, "2025 AI in Insurance: The C-Suite Verdict." About 90% of insurers with generative-AI work underway, ~55% in early or full deployment. conning.com
- 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
- McKinsey, "The future of AI for the insurance industry." AI across underwriting, claims, and customer-service operations. mckinsey.com
- NAIC, "Model Bulletin: Use of Artificial Intelligence Systems by Insurers." Calls for a written, validated, monitored AI program. naic.org
- Locke Lord / InsureReinsure, "Wisconsin Becomes the 24th State to Adopt the NAIC Model Bulletin." State adoption count as of March 2025. insurereinsure.com
- Voiso, "Average After-Call Work Time." After-call work as 6 to 12% of agent time. voiso.com
- 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