How we engage. One mode per stage of the work.
Three modes, chosen by where you actually are on AI. Not a generic methodology. Not a fixed scope. The engagement shape follows the maturity of the team it's serving.
Starting
No AI central function yet. Early proof-of-concepts. The stack is fragmented and the roadmap is under pressure.
Scaling
AI leadership exists. Enablement doesn't scale. A small central team is being asked to serve every function at once.
Operating
AI runs across production workflows. The question is no longer adoption. It's durability, drift, and distribution.
Mode 01 · StartingNo AI central function yet.
The first proof-of-concepts are already happening somewhere in the org. Nobody agrees on what's working. There's no registry, no rollout playbook, no one obvious to own the next decision.
This is where Airframe stands up the AI center of excellence. We map what your teams are actually running into the Registry, pressure-test the roadmap against Research on what's deploying in peer companies, and stand up the first Transformation deployments so the early work has structure instead of sprawl.
What the engagement looks like
- Stack audit against the 17,000-product Research base, mapped into your Registry. Free to start.
- Roadmap session grounded in Research. Peer benchmarks, not guesses.
- Senior operators from Airframe stand up the Registry and the first Transformation deployment.
- A working AI CoE structure, co-owned with your team.
Mode 02 · ScalingEnablement is the bottleneck.
Your AI leadership is in place. The function exists. The function can't scale. One small central team is being asked to serve marketing, finance, operations, engineering, and legal simultaneously, and the queue keeps growing.
Airframe's senior operators run alongside your central team and move the enablement work outward. They distribute Research into the hands of the people making decisions. They ship Transformation work into the workflows where work actually happens, with Context wiring the registry and corpus into Claude, ChatGPT, Cursor, and Slack so it shows up where the team already is. The central function stops being a service desk and starts being a platform.
What the engagement looks like
- Operator pairing with each internal workstream. Weekly rhythm.
- Context distributes Research directly into the tools your teams already use, including Claude, ChatGPT, Cursor, and Slack.
- Transformation rollout into your runtime, not a sandbox.
- Renewals watches drift, usage, and contract exposure across every team you've stood up.
Mode 03 · OperatingAI is already in production.
You're past adoption. The agents are running. Teams are shipping with them. The problem set has shifted. Drift. Cost discipline. Renewal timing. Model deprecations. Distribution across the surfaces your team actually works in.
Airframe becomes the tooling layer underneath what you already run. Renewals reads the contract surface and flags drift before the bill hits. Transformation keeps shipping briefs, deep dives, and renewal recommendations against your live Registry. Context distributes the registry and the Research corpus as an always-on feed into the surfaces your team works in. Your internal team keeps the reins. Airframe keeps the intelligence current, continuously.
What the engagement looks like
- Renewals tracks your full stack, flagging drift and contract exposure before the bill hits.
- Context distributes your Registry and Research corpus into Claude, ChatGPT, Cursor, and Windsurf.
- Transformation briefs on what's emerging in peer companies at your maturity.
- Senior-operator access on retainer for the high-stakes calls your team wants a second read on.