AI Enablement
AI access didn't make your team AI-native.
Most revenue teams bought the tools and saw nothing change. The gap was never access - it's whether your team can build with what they have. We embed for 4-8 weeks and close it.
Book a CallThe gap
Everyone has the same tools. The gap is what they do with them.
87% of sales orgs already use AI. Most revenue leaders will tell you it hasn't moved the number. The difference between teams was never access - it's whether they turned tools into a system, and whether the team can run that system on their own.
Chatting
Reps paste prompts into ChatGPT to reword emails. The output is faster, but nothing about the workflow changes. Most teams are here and think they have climbed.
Tooling
A few custom prompts and scripts float around the team. They help, but quality is uneven and nothing is shared. The org gets one person's leverage, not the team's.
Compounding
The team runs a real system. Research, scoring, enrichment, and outreach are built once and reused. Every workflow makes the next one cheaper to build.
The gap between the teams compounding and everyone else gets wider every quarter. The tools won't close it. Enablement will.
What we actually sell
We don't teach tools. We build the capability, then hand it over.
Most AI training teaches prompting in isolation. It doesn't stick. People sit through a workshop, go back to their inbox, and nothing changes.
What sticks is building real workflows on your real problems, with us in the room, then documenting them so your team can evolve them after we leave.
And the work is never finished. Models change every few months, costs shift, and a workflow that worked last quarter breaks. A system someone hands you goes stale the moment the ground moves. A team that can rebuild it doesn't.
That's the whole difference. When a traditional agency leaves, you start from zero. When we leave, your team has the working systems, the SOPs they wrote, and the muscle to build the next thing themselves. Retention should come from wanting our speed, not needing our knowledge.
The model
Not everyone needs a terminal. Two tracks, matched to two roles.
Cowork
SDRs, AEs, marketing
A visual interface. Plain English, no terminal. The team runs account research, pre-call briefs, CRM updates, outreach drafts, competitive intel, and list cleanup the same way they'd brief a teammate.
Claude Code
RevOps, ops, technical builders
Where the engine gets built. Lead scoring models, multi-source enrichment, and custom workflows - then packaged as tools the rest of the team runs from Cowork. The builder builds once; the team runs it forever.
A small group builds the AI. It shows up where everyone already works. That's the whole model - and it's why decentralized "everyone learn AI" programs stall while this one compounds.
The engagement
Four to eight weeks. Built on your data, not toy examples.
Assessment
Leadership commits to one measurable goal. We map each role's real workflow, find where AI is and isn't helping, and pick 3-5 high-impact problems to solve.
Foundations
Both tracks set up and connected to your CRM, email, and Slack. Your team encodes its ICP, messaging, and positioning into a context layer the whole team draws on - the onboarding doc your AI reads every session.
Co-build
Each week: pick a problem, design the workflow, build it live as a team, test on real data, write the SOP. We lead the hard parts. Your team owns the output.
Handoff
Every workflow has an SOP your team wrote, not us. Then the independence test: each person builds a new workflow from scratch, with us out of the room.
What compounding looks like
The point isn't one good workflow. It's that each one makes the next cheaper.
In a single week, our own team shipped:
- A Google Maps scraper for local lead sourcing
- Skills for finding intent signals and writing outbound from context
- One-click campaign launches into our sending platform
- A news feed that turns articles into lead signals
- All validation in Sheets - cutting 40 hours a week of manual logging
Each build lives in the system. Next time, the AI already knows how you did it last time. That's what your team walks away with - not a folder of prompts, a machine that gets smarter every week.
See it in practice
What a real GTM system on AI actually looks like.
Most teams that call themselves "AI-native" are drafting emails faster. This is the difference - a full walkthrough of running GTM on a real system:
- Scoring tens of thousands of prospects in minutes
- Pulling sales call transcripts into context automatically
- Prospect research your $15K B2B database can't give you
- When to build it yourself vs. when to buy
The outcome
What your team owns when we leave.
- Working AI workflows built on your data - in both Cowork and Claude Code
- An SOP for every workflow, written by your team and reviewed by us
- A context layer that encodes your GTM intelligence
- Custom tools your RevOps built, that the whole team runs
- The ability to build the next workflow without us in the room
Go deeper on how the system compounds:
Stop renting AI capability. Build it in-house.
We embed with your team for 4-8 weeks to co-build AI workflows on real problems - and leave them able to build their own.
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