The AI layer that finishes the call after the call
NextRev sits on top of the dialer and CRM your floor already runs. During the call it keeps a live cheat sheet of what is captured, missing, or unsure. After the call the summary, the CRM record, and the paperwork draft themselves and wait for one-click approval. Nothing sends until an agent says so.
How it works
It works in two beats: while the agent is on the phone, and the moment the call ends. The flow stays the same whether your dialer hands over live audio or just the recording.
- During the call: a live cheat sheet shows each field as captured, missing, or unsure
- The summary writes itself from what was actually said on the call
- The CRM record fills in and statuses update across your systems
- Applications and enrollment paperwork draft themselves for review
- Everything lands in one place, queued for one-click approval
- Runs on the dialer and CRM you already have. No rip-and-replace
Nothing sends on its own. The agent's approval is the only point a record moves. This matters because the work being removed is real: industry benchmarks put after-call work at roughly 6 to 12% of an agent's paid time, and studies of manual data entry report errors of roughly 0.3% to 6.6%. Those are sourced figures for the status quo, not a NextRev result. We size yours from your own calls.
What the platform gives your floor
Four capabilities, all on the systems you already run.
A live cheat sheet, during the call
Captured, missing, and unsure fields on screen as your agent talks, so they stay with the customer instead of hunting for fields.
The work, already drafted, after the call
The summary, the CRM record, and the application draft themselves and queue for one-click approval. Your agent reviews; nothing sends on its own.
Runs on the stack you already have
Whether your dialer hands over live audio or just the recording, the value never depends on the hardest piece to connect.
Provenance, not generation
Every field traces to a moment in the call. Anything unsure is flagged, never invented. A private-cloud or local option is available; the rules are set at diagnosis.
Why now
The technology to do this safely has arrived, and the cost of the manual status quo is well documented. The numbers below are industry context, not NextRev results.
Industry analysis estimates gen AI applied to customer-care operations is worth 30 to 45 percent of current function costs, much of it from drafting summaries and notes. That sizes the status-quo cost, not our lift.
In a study of over 5,000 support agents, an AI assistant raised issues resolved per hour by about 14 percent, and roughly 34 percent for newer agents. Industry evidence on AI assistance, not a NextRev figure.
Across insurers, contact-center and customer-service automation is among the most reported AI uses, with carriers testing routing and next-best-action. The tools are reaching insurance floors now.
See it run on your own calls
We start with a diagnosis sized on your floor's own numbers. You leave with a plan whether or not we build.
Request a DemoSources
- Brynjolfsson, Li and Raymond, "Generative AI at Work" (NBER Working Paper 31161). AI assistant raised issues resolved per hour about 14% on average, 34% for newer agents. nber.org
- McKinsey, "The economic potential of generative AI: The next productivity frontier." Gen AI in customer care valued at 30 to 45% of function costs; up to 50% fewer human-serviced contacts. mckinsey.com
- McKinsey, "The future of AI in the insurance industry." Contact-center and customer-service automation among the most reported AI uses by insurers. mckinsey.com
- McKinsey, "The state of AI 2025." Contact-center automation among top AI use cases across industries. mckinsey.com
- Voiso, "Average After-Call Work Time." After-call work as roughly 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