Every business is solving the same puzzle.
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 problems — the stack, the process, the parts that don't talk to each other — and I use AI to synthesize them into a system that works, built from what I've seen inside real RevOps and GTM operations, not a framework off a slide.
Complexity
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.
Synthesis
I take what’s scattered across your stack, your team, and your process, and turn it into one system that actually fits together.
AI
AI does the heavy lifting — reading, structuring, connecting — so the solution ships in days, not quarters.
Tailored work, not a fixed package
Six shapes the work usually takes. Every engagement starts from your actual stack and process, not a template.
Lead scoring
AI-scored queues that tell reps who to call first, not just who’s next.
MVP creation
Working tools shipped in days, not quarters, built for the workflow you actually have.
Sales enablement
Answers built into the tools reps already use, so the team stops being the bottleneck.
CRM cleanliness
Deduplicated, standardized, and guarded so the mess doesn’t come back in six months.
Workflow automation
Handoffs between teams that carry context automatically, instead of starting from zero.
AI-powered data intelligence
The same synthesis engine behind this page: reading a market at scale and turning it into a clear signal.
Five kinds of puzzle, opened up
The shapes this work usually takes: what the situation was, what I built, and what the fix actually involved. Pick one from the index.
Real engagement patterns, described generally — specifics vary by client
Lead scoring, not lead luck
Reps were working leads in the order they arrived, not the order they’d actually convert — every lead treated as equally worth a call, and the best ones getting lost in the queue.
An AI-scored qualification layer that ranks incoming leads against the signals that actually predicted a close, then routes the top of the queue straight to reps inside the CRM they already use.
What this involved- Defined the signals that mattered, not firmographic guesses
- Scoring logic reviewed with the team, not left as a black box
- Routing wired directly into the CRM — no separate tool to check
- Retrained as new data came in
Read like a rate, not a part count
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 current market averages on a 0–100 scale
Five functions, one train of wheels
Marketing
A definition of a qualified lead that survives contact with Sales.
Sales
A pipeline that reflects reality without a weekly negotiation.
Customer Success
A handoff that carries the promise made during the sale.
Finance
One forecast, one source, and no reconciliation ritual.
Product
Usage evidence that reaches the roadmap before the churn does.
Assembled once, running since
Four stages, no human in the loop — the same kind of AI-run pipeline I build for the operations I work on. It doesn't replace judgment, it removes the busywork standing between a question and an answer. Open a stage to read it.
A scheduled n8n workflow pulls RevOps postings daily from LinkedIn and other sources. The ingestion layer runs without supervision, so the record grows whether or not anyone is watching.
LinkedIn via Apify · Adzuna API · SerpAPI
Why I build it this way
I work inside Revenue Operations — 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: that reading a market at scale doesn't require a team, it requires the right use of AI. Everything on this page — the scoring, the automation, the case work — runs on that same principle. It's the same one I bring to the businesses I work with.
Bring me the problem. I'll bring back the system.
Whatever's scattered across your stack, your team, or your process — send it over. I'll synthesize it with AI into a system that actually runs, the same way this page and the record behind it were built.