Net new pipeline generated
GTM systems for teams where the cost of drift is already visible
Build the GTM AI system your revenue team can actually run.
Most teams do not need another AI strategy. They need someone to decide what gets built, put the controls around it, and make sure people use it.
Confluent: built the GTM AI program from zero, including the team, governance, systems, and adoption model.
How the work changes
A forecast call, handoff, or AI request arrives without enough shared context.
Selected experience
Confluent GTM AI built from zeroBox$18M pipeline52% fewer access tickets
Selected outcomes from prior GTM systems work
GTM systems spend reduced
Fewer access tickets
Tell me what is costing you confidence or time.
I start with the workflow people are already working around.
A forecast you can’t defend
Handoffs that leak
Tools people work around
Ownership no one can see
AI operations
I turn AI activity into work your team can trust.
I keep experimentation open, then build the intake, context, permissions, review, and handoff around the work worth scaling. The deliverable is a team that can run it, not an agent catalog.
See what the program changes- Inventory and focus. Find duplicates, owners, and the work worth capacity.
- Design the control path. Set context, permissions, human gates, and escalation.
- Ship and retool. Measure adoption, quality, time returned, and higher-leverage ownership.
What I build around AI
An agent is only one piece.
Explore the context, access rules, review steps, and handoffs that make an AI workflow safe to use on a Tuesday.
Layer 01
Governance
Decide what is allowed, who owns the decision, and when it must be reviewed.
Try the diagnosis
Start with the workflow, not the tool.
Choose the constraint that sounds familiar. The useful move is usually smaller and more specific than a transformation program.
Experiments multiply without shared prioritization or permission boundaries.
Create an intake rubric, context standard, human gates, and production path.
The team knows what deserves capacity and what should stop.
A useful first move
Tell me where the system is leaking.
You do not need to know the answer before the first call. Pick the constraint and I will start there.
Read it your way
One system, three useful levels of detail.
The decision, the risk, and the measurable change.
Trace the decision
See where the human call happens.
Follow one messy signal through context, access, review, and a result someone can stand behind.
01 / Messy signal
A rep flags a deal risk in a call note, forecast comment, or handoff.
Failure lab
The edge cases are the product.
Click through the ways an apparently successful AI workflow fails in production.
What breaks
The agent sounds confident because the source is old.
Fix: Add freshness checks, source provenance, and a hard stop when the required context is stale.
Who this work is for
For founders, CROs, and COOs
When the operating constraint is expensive but the right systems owner is not in seat yet.
For RevOps and GTM Systems leaders
When stack debt, handoffs, and decision rights are slowing the teams you already support.
For AI program owners
When experiments are multiplying and scaled work needs governance, enablement, and proof.
Consulting engagements
AI Operations Program
Turn scattered AI experiments into a governed portfolio with a production path for intake, prioritization, evaluation, enablement, reliability, and measurement.
One accountable path for team-wide AI work without blocking local experimentation
See the programGTM Systems Diagnostic
Find where signal, ownership, or workflow breaks. Leave with an executive readout and a prioritized 90-day roadmap.
A shared explanation of what is actually breaking
See the diagnosticRevenue Execution Sprint
Redesign and implement a critical forecast, handoff, routing, or operating workflow with the people who will run it.
A critical workflow that is easier to run and inspect
See the sprintFractional RevOps Leadership
Add senior RevOps and systems ownership while the team stabilizes execution and builds internal capacity.
Senior ownership without waiting for a full-time search
See the retainerStart with the work I actually owned.
I show the mandate, the choices I made, and the result. Confluent is the clearest example: I hired the team and built the GTM AI program from zero.
Browse the proofResume-backed outcomes
Net new pipeline generated
GTM systems spend reduced
Fewer access tickets
Sales productivity increase
Increase in qualified opportunities
Faster lead-to-follow-up
Personal ownership
Built the Confluent GTM AI program from zero and hired the team that delivered it.
Documented scale
$12M annual value across 190+ agents and automations, including 20 multi-agent workforces.
Evidence boundary
Resume-backed operator case study; no client testimonial or confidential claim is implied.
What I will not claim
I will not manufacture a logo, quote, metric, or before/after story without permissioned source material.
Representative systems work
Forecast Quality Dashboard
A dashboard designed to surface forecast risk and pipeline confidence by segment.
Read the case study
ATX Untangled
Independent help for Austin residents to save money, solve city problems, and act on public data.
Read the case study
Rumor → Reality Launch Radar
Built and shipped in one night: an evidence engine that checks startup rumors against official signals and explains each result.
Read the case studyBuild notes
What broke, and what I changed.
The decisions behind the work, without the agency-case-study gloss.
The decision
Do not start with an agent catalog. Start with the expensive workflow and its owner.
The thing that broke
AI activity outpaced shared context, permissions, review, enablement, and measurement.
The repair
I connected intake, risk, access, human review, adoption, and handoff in one path.
The rule
If nobody can explain what changed, who owns it, or how it is measured, it is not ready.
I stay close enough to own the hard parts.
I do not hand over a strategy deck and disappear. I build with the people who inherit the work.
How the work moves
Diagnose the operating truth
Design the smallest system that works
Build with operators
Instrument reliability, adoption, and handoff
Best fit
A B2B team with a real operating constraint, an executive sponsor, access to operators and data, and willingness to change process—not just tools.
Not a fit
Strategy theater, a tool implementation without an owner, free speculative work, or permanent staff augmentation without a mandate.
Before we talk
The useful answers up front.
What do you actually build?
Governed GTM AI programs, RevOps systems, forecast and handoff workflows, and the operating cadence that keeps them useful after launch.
Who is this for?
Growth-stage and enterprise B2B teams with an executive sponsor, a costly operating constraint, and enough access to change the workflow behind it.
Can I start smaller?
Yes. Start with a bounded diagnostic when the constraint is real but the right build sequence is not yet clear.
Show me the workflow.
Bring the thing your team is still doing by hand. We will decide if I am the right operator to change it.