How we prove the value before you build
We do not open with someone else's case study. We open with a paid diagnosis sized on your own numbers. You see a measured baseline, the opportunity in your own hours, and the first piece that pays for itself, often live in weeks. You keep the plan whether or not we build.
Why we lead with proof, not a slide
A polished case study with someone else's logo tells you nothing about your floor. The honest version is harder and more useful: measure your own numbers first, prove one piece, and let the result decide the rest.
Across around 300 public AI deployments, an MIT study reported that roughly 95% of enterprise generative-AI pilots delivered no measurable business return. That is the cost of buying first and measuring never. It is the status quo we are built against, not a NextRev result. We measure yours before anyone commits to a build.
The sequence: diagnose, prove, build, own
Four steps, in order. Each one earns the next. You can stop after any of them and still walk away with something you can use.
Diagnose (paid Gate-0)
A short, paid diagnosis sized on your own floor. We measure a baseline from real calls and records over a recent window, then size the opportunity in your own hours and dollars.
Prove
The first piece goes live, often in weeks, and pays for itself against that baseline. We read the before and after so the change is attributable, not assumed.
Build
Only what the diagnosis justified. Everything is prepared from read-only copies of your own systems and handed to your team to deploy. Nothing touches a live system on its own.
Own
The baseline, the plan, and the working pieces are yours. They run on the stack you already have, so you are never locked to us to keep the value.
What a proof engagement produces
Whether or not we go on to build, the diagnosis leaves you with concrete artifacts:
- A measured baseline: real numbers from your own calls and records
- A sized opportunity: hours and dollars at stake, in your own terms
- A short list of the highest-return pieces, ranked
- A before-and-after plan to read whether each piece actually moved the number
- The first self-paying piece prepared from read-only copies of your systems
- A handoff your own team can deploy, with no lock-in
Read-only means we work from copies, never inside your live systems, and the rules for handling your data are set with you at the diagnosis. The plan is yours to keep and to run with another partner or your own team if you prefer.
How we size the opportunity
The method is borrowed from how disciplined operators measure any investment: a clear baseline first, conservative assumptions, and a tracked result.
We log real instances over a recent window and use observed numbers, not best-case guesses, then apply conservative savings assumptions.
After-call work runs roughly 6 to 12% of an agent's paid time across the industry, higher on compliance-heavy lines. We size yours from your calls, not the benchmark.
Defining value up front and tracking clear KPIs is the practice most tied to bottom-line impact, yet industry studies find fewer than one in five organizations do it.
The sizing method follows McKinsey's guidance to measure AI with the rigor of any capital investment: define value up front, build attribution into the rollout through staggered or A/B comparison, and review against a fixed cadence. The benchmark figures here are sourced industry context, not a NextRev result.
See it on your own numbers
Request a walkthrough. We start with a paid diagnosis sized on your floor's own numbers, and you leave with a plan whether or not we build.
Request a walkthroughSources
- MIT Project NANDA, "The GenAI Divide: State of AI in Business 2025," as reported by Fortune. Roughly 95% of enterprise generative-AI pilots showed no measurable return. fortune.com
- McKinsey, "From promise to impact: How companies can measure and realize the full value of AI." Measure AI like a capital investment; define value up front; build attribution into rollout. mckinsey.com
- McKinsey, "The state of AI" 2025. Tracking well-defined KPIs is the practice most tied to bottom-line impact; fewer than one in five organizations track them. mckinsey.com
- Voiso, "Average After-Call Work Time." After-call work as roughly 6 to 12% of agent time. voiso.com
- SG1 Consulting, "AI Automation Pilot ROI Methodology." Establish a baseline from the last 4 to 8 weeks of observed numbers; conservative savings assumptions. sg1consulting.com.au
- SQM Group, "Industry Standards for Top Call Center KPIs." Call-center KPI benchmarks. sqmgroup.com