Everything NextRev does, after the call and during it
NextRev rides along on the call, then finishes the work that usually starts when the call ends. The summary, the structured fields, the CRM record, and the paperwork draft themselves from what was said. Your agent reviews and approves. Nothing moves until they do.
The feature set
Six things NextRev does, one schema underneath them. It runs on the dialer and CRM you already have.
Live cheat sheet during the call
As your agent talks, NextRev shows which fields are captured, which are still missing, and which it is unsure about, on one panel. The agent stays with the customer instead of digging through forms. It reads from the call. It does not script what the agent says.
Post-call summary and field extraction
When the call ends, NextRev drafts the summary and pulls the structured fields out of what was actually said. Each field traces back to a moment in the call, and anything it is unsure about is flagged rather than guessed. Contact-center vendors report that AI-generated summaries cut after-call note time, with accuracy depending on audio quality and vocabulary.
Automatic CRM write-back
Approved records write back into the CRM and dialer you already run, into the fields you already use. Rule-based write-back applies the same mapping every time, which removes the copy-paste variance of hand entry. Nothing writes until an agent has approved it.
Document and application autofill
The same extracted fields fill applications, enrollment forms, and follow-up documents, so the agent reviews a draft instead of starting from a blank form. Every value on the page points back to where it came from in the call, so a reviewer can check it against what was said.
Review, approve, and send
Every summary, record, and document queues for the agent to read and approve. The agent is the only commit point: their approval is the single moment anything moves. NextRev never sends, writes, or files a record on its own.
One field schema underneath
A single field schema defines what to capture, where it maps in the CRM, and how it fills each document. Define a field once and it flows through extraction, write-back, and paperwork, so the three never drift out of sync as your forms change.
Why this shape, and where the industry is
The numbers below are industry figures, framed as the cost of the status quo. They are not NextRev results. We size yours from your own calls.
After-call work runs roughly 6 to 12% of an agent's paid time on many floors, and higher on compliance-heavy insurance lines. That is the time the feature set above aims to give back.
A systematic review puts manual data-entry error at roughly 0.3% for keyed entry, up to 6.6% for record abstraction. Rule-based write-back applies the same mapping every time, which is why automation reduces that variance rather than relying on a tired agent at hour eight.
Agentic AI is arriving quickly in customer service, but a regulated record cannot move on a guess. NextRev writes only what traces to the call, and only after an agent approves.
For where the industry is heading: Gartner has projected that conversational AI could reduce contact-center agent labor costs by around $80 billion by 2026, and McKinsey reports generative AI could improve customer-care productivity by an estimated 30 to 45% of function cost, directionally large though uneven in practice. We could not open those two pages directly, so we state them as the analysts' projections, not as facts and not as our lift.
See it on your own calls
We start with a paid diagnosis sized on your floor's own numbers. You leave with a plan whether or not we build.
Request a DemoSources
- 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
- Oracle, "What Is Robotic Process Automation (RPA)?" Rule-based automation executes the same steps consistently, reducing data-entry error. oracle.com
- NICE, "Call Summary Automation in Contact Centers." AI-generated post-call summaries reduce after-call work and agent note-taking. nice.com
- Gartner, "Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026" (press release; projection, not confirmed by us). gartner.com
- McKinsey, "The economic potential of generative AI." Gen AI productivity potential in customer care, framed as industry estimate. mckinsey.com