AI enablement
AI enablement that changes how your developers build.
Hands-on training with AI coding tools in your own codebase, guardrails your security team signs off on, and pairing on real work until AI-assisted development is simply how your team ships.
Why rollouts stall
Buying AI tools is easy. Getting value from them isn’t.
- 01
Licenses without habits
Teams get AI coding tools but no time to learn them, so usage fades after the first month.
- 02
Demos that don’t transfer
Generic training uses toy projects. Developers go back to a real codebase and their old habits.
- 03
No guardrails, no green light
Security and legal can’t approve what hasn’t been defined, so adoption waits.
- 04
No way to show the return
Without a baseline, leadership can’t tell whether the spend is paying off.
The program
Training on real work, with guardrails and a baseline.
Every program has the same four parts, sized to your team and your stack.
- Hands-on workshops
- Coding agents, AI-assisted review, and test generation, taught in your repositories against your real backlog. Sessions are split by role: developers, reviewers, and tech leads.
- Playbooks and guardrails
- Which tools fit which work, what code AI can touch, what never goes into a prompt, and how AI-written code gets reviewed. Written with your security team, so adoption has a green light.
- Build alongside your team
- We pair with your developers on a live project, so the skills transfer on real work and the first win ships while we are still there.
- Measure the change
- We baseline cycle time, throughput, and review load before training, then measure again after, so leadership sees the return in numbers.
What developers learn
Practical skills for AI-assisted development.
We teach practices that outlast any single tool, so your team keeps the gains as the tools change.
Developers leave able to
- Planning work and delegating it to coding agents
- Reviewing multi-file AI changes with confidence
- Generating tests and raising coverage
- Refactoring and working in legacy code
And they know how to
- Giving tools the right context: specs, files, and constraints
- Writing documentation and onboarding guides
- Keeping secrets and customer data out of prompts
- Knowing when not to use AI
How it runs
About six weeks from baseline to handoff.
- Week 1
Baseline
Measure current cycle time and review load. Pick one real project to carry through the program.
- Weeks 2–3
Train
Role-based workshops in your repositories, plus the playbook and guardrails your security team signs off on.
- Weeks 4–6
Pair
We build alongside your developers on the chosen project until the first AI-assisted release ships.
- Handoff
Measure
Compare against the baseline, update the playbook, and hand ownership to your internal champions.
Beyond engineering
AI training for business teams, too.
Finance, legal, and operations teams get the same practical approach: training on their own work, a clear usage policy, and an approved-tools list your security team is comfortable with.
Finance
- Variance commentary drafts
- Reconciliation checks
- Report summaries
Legal
- Contract term summaries
- Clause comparisons
- Research first drafts
Operations
- Document intake
- SOP and policy answers
- Meeting and status notes
Questions and answers
Questions about AI enablement
What is AI enablement?
AI enablement is the work of getting a team from having AI tools to actually using them well: training on real work, clear guardrails, and a way to measure the result. For engineering teams, that mostly means AI-assisted development with coding agents and AI-enabled editors.
Which AI coding tools do you train on?
The tools you already license, or the ones we help you choose. That typically includes coding agents such as Claude Code, AI-assisted editors such as Cursor, and assistants such as GitHub Copilot. The practices we teach carry over as the tools change.
Do you work in our actual codebase?
Yes. Training happens in your repositories, under your access controls and a mutual NDA. That is the difference between a workshop people forget and habits that stick.
How long does an enablement program take?
A typical program runs about six weeks from baseline to handoff. Larger organizations often start with one team, then roll the playbook out to others.
How do you measure results?
We take a baseline before training, such as cycle time, throughput, and review load, then measure the same things after. You get the before-and-after numbers, not just attendance.
Is AI enablement only for developers?
No. We also run practical AI training and usage policies for finance, legal, and operations teams. The developer program is where the timeline gains are largest, so most clients start there.
Start with one team
Pick one project. We’ll help your team ship it with AI.
A short conversation about your stack, your tools, and where your developers lose the most time.