The hard part was never any single tool, process, or hire. It's fitting all the pieces together into something that actually runs, while the pieces themselves keep changing underneath you.
That's the work I do. You bring the stack, the process, the parts that don't talk to each other. I use AI to turn them into a system that works, built from what I've seen inside real RevOps and GTM operations, not a framework off a slide.
GTM and RevOps run on more tools and more data than any one team can track. That’s not a failure on your part. It’s just where the market is.
I take what’s scattered across your stack, your team, and your process, and turn it into one system that actually fits together.
AI does the heavy lifting. It reads, structures, and connects, so the solution ships in days, not quarters.
Six shapes the work usually takes. Every engagement starts from your actual stack and process, not a template.
AI scored queues that tell reps who to call first, not just who’s next.
Working tools shipped in days, not quarters, built for the workflow you actually have.
Answers built into the tools reps already use, so the team stops being the bottleneck.
Deduplicated, standardized, and guarded so the mess doesn’t come back in six months.
Handoffs between teams that carry context automatically, instead of starting from zero.
The same synthesis engine behind this page: reading a market at scale and turning it into a clear signal.
Company names stay out. The problem, what I built, and what changed do not. Bracketed figures are ones I still need to fill in.
Reps worked the queue in arrival order, not conversion order.
Payouts lived in spreadsheets, exceptions, and delayed cycles.
Regional managers could not see coverage or whitespace without waiting on a monthly export.
A weekly ops process was stuck in a spreadsheet that had outgrown it.
I do not publish client quotes I did not earn in writing. Until those exist, this is how the engagements are run.
The job is not the ticket. It is whether the queue, the payout, or the territory view still works after I leave.
If the next person has to reverse engineer it, I did not finish. Logic, owners, and the why sit next to the build.
Names, numbers, and screenshots stay inside the engagement unless you say otherwise. What you see here is anonymized on purpose.
A working v1 in the team's hands beats a perfect spec. We ship, we watch it get used, we tighten.
A chronometer is not certified on how many parts it holds. It is certified on how far it drifts per day. We score businesses the same way.
Counts the disconnected platforms one role must hold together. A reading of complexity bought and not yet resolved.
Counts the distinct skill categories demanded of one person. A reading of design a company asks a single hire to carry.
Hands show the averages across all 1,451 scored postings, on a 0 to 100 scale
A definition of a qualified lead that survives contact with Sales.
A pipeline that reflects reality without a weekly negotiation.
A handoff that carries the promise made during the sale.
One forecast, one source, and no reconciliation ritual.
Usage evidence that reaches the roadmap before the churn does.
I work inside Revenue Operations. That's the function that sits at the center of every handoff between Marketing, Sales, and Customer Success, and feels every gap between them first. That vantage point is where this page comes from.
RevStack started as a way to prove a point. Reading a market at scale doesn't require a team, it requires the right use of AI. The scoring, the automation, and the case work on this page all run on that same principle. It's the same one I bring to the businesses I work with.
Send over whatever's scattered across your stack, your team, or your process. I'll synthesize it with AI into a system that actually runs, the same way this page and the record behind it were built.
