A decade building systems that change behavior at scale.
I sell B2B in the mid-market for a living. I built Raveworthy on my own. Here's the proof, layered.
I sell for a living.
Mid-market seller. Multi-stakeholder deals daily. Procurement, finance, security, ops.
I run the AI-assisted playbook I install for other teams.
I built the system before I sold it.
Built Raveworthy on my own time. Ten-plus AI workflows across the deal cycle: pre-deal intelligence, discovery prep, qualification, demo prep, business case, close, and the boring admin work nobody wants.
Every workflow tested on my live deals first. Refined until it held up under real pressure.
The version you get is the same one I use Monday morning.
Multiple sales teams. Not a sandbox.
I've installed Raveworthy across multiple real engagements. Mid-market B2B sales teams. Multi-location franchise operators running their own sales motions. Independent sales practices.
Eight sessions. 30+ reps and operators trained. Each session 30 minutes. No slides. Live deals worked using the workflows in real time. Documented session-over-session adoption.
The version you install is the one refined across all of these teams.
Built around a behavior problem, not an information problem.
Before sales, I spent a decade designing training systems used across thousands of fitness studios worldwide. The work was never about transferring information. It was about changing how people did the work.
AI sales training is the same problem in a different suit. Most attempts treat it as an information problem. Reps walk out informed. Six weeks later, nobody's using the tool.
What outcomes look like at week 4 and beyond.
Real revenue impact takes 60-90 days. Here's what we measure when, and how it ladders to revenue.
Leading indicators (week 4)
Captured in the outcome report.
- Adoption rate per rep
- Sessions per rep per week
- Behavior change (% of deals with structured docs)
- Pre/post confidence survey
Lagging indicators (day 60-90)
Captured in optional follow-up review.
- Pipeline created per rep
- Sales cycle time
- Win rate on AI-assisted deals vs control
- Ramp time for new hires
The honest expectation
- Adoption data by week 4. Pipeline data by day 60. Revenue data by day 90-120. Leading indicators aren't revenue.
Not the license. The lost ground.
Most mid-market sales orgs rolled out ChatGPT, Claude, Copilot, or Gemini in the last six to twelve months. Industry-average adoption sits at 10-20%.
That's not the real problem.
The real problem is competition. Other sellers are learning these tools every day. Moving faster. Building stronger pipeline. Closing more deals. Compounding every quarter.
Every quarter your team doesn't structure AI usage, the gap widens.
The install closes it. Same license your team already pays for, turned into actual sales productivity.
As usage scales, costs will scale. That's a high-class problem. We address it when it shows up, not before.
For the CFO
The math is boring. The Install pays for itself when the team saves 100 rep-hours. Most hit 5-10x that in 60 days.
For Security and IT
Tool-agnostic. We use your existing AI platform. No new vendor. No new data flows. Security signs off on every custom workflow before it ships.
For the Executive Sponsor
Documented case study. Reference checks available. If adoption metrics don't move by week 4, you get half your money back. I'd rather take the loss than collect on a flat install.
More case studies in flight. I'll publish them when they're worth publishing, not before.