Most AI sites lead with big, unverifiable percentages. We won't. Below is how we actually measure results, plus representative examples of the engagements we run, clearly labeled as illustrative until we publish named client outcomes with permission.
AI-Disruptors measures results against a baseline captured before the work starts, lead-response time, dropped-lead rate, hours on a repetitive task, review velocity, so any change is real and attributable. The case studies below are representative and illustrative, not invented clients, and we don't publish fabricated metrics. Named client results are added only with the client's permission and real numbers.
Situation: After-hours and missed-call leads waited hours for a reply; many got no second touch.
What we built: Instant lead response + after-hours capture, then structured follow-up, measured against the prior week's baseline.
The point: Faster first response and fewer dropped leads: the fastest, lowest-risk place most service businesses can start.
Situation: Manual intake, proposals, and follow-up ate hours that should have gone to billable work.
What we built: AI-assisted intake, proposal drafting to a firm-approved template, and CRM follow-up, with a human approving every send.
The point: The team covers more ground without adding headcount, and nothing goes out unreviewed.
Situation: Execution varied by site and reporting lived in separate tools, so problems surfaced late.
What we built: A standardized follow-up workflow proven at one location, then rolled out, plus a unified reporting view.
The point: Consistency across locations and earlier visibility into what's leaking, governance and rollout matter as much as the tech.
Situation: Patient follow-up and review requests depended on whoever remembered.
What we built: Automated appointment follow-up and review requests at the right moment, with unhappy feedback routed privately, inside the practice's compliance boundaries.
The point: A consistent patient-communication system instead of a memory-dependent one.
Before anything is built, we capture the current number, response time, dropped-lead rate, hours on a task, review pace, so change is measurable, not a guess.
We ship the single highest-leverage workflow and track it against that baseline, rather than claiming credit for everything at once.
You get the real number, in plain language. If something didn't move, we say so and adjust. No inflated percentages.
While our own client numbers stay private until we can publish them with permission, third-party research points the same direction, see the sourced figures and links on the homepage's track record section. Presented there as industry context, not as AI-Disruptors client results.
The examples here are representative and illustrative: composites of common situations we work on, clearly labeled. We don't publish invented client names or fabricated metrics. Named client results are added only with the client's permission and real numbers.
Against a baseline captured before the work starts, lead-response time, dropped-lead rate, hours on a repetitive task, or review velocity, so any change is attributable and honest.
Because outcomes vary by business and we won't publish inflated numbers. Where we cite AI-impact statistics, they are third-party research with sources, not claims about our own clients.
We'd rather show you exactly how we'll measure the work than hand you a percentage you can't verify. When we have named client results we can publish, they'll appear here, with real numbers and permission.
The complimentary Snapshot gives you a directional read; the Assessment turns it into a measured plan with a clear first move.
