Build teams that solve problems end to end.
Every cycle starts with a real-work simulation: each person sees exactly where they stand, the training targets exactly that, and the next baseline shows the movement.
- Does the team get faster
- Can the team build this
- Works past engineering
What leadership expects from AI spend.
Each person ships more of the work than before AI, with less senior time spent reviewing it.
Work that moves from pilot to production, and keeps working once it is there.
Spend that maps to what actually reached the work.
All three depend on the same thing: the team's AI capability. That is what Cadra baselines and builds.
71% of people already use AI at work. 15% of teams use it fully.
AI-native is two different jobs.
Can the team design, ship, and run AI systems that hold up in production?
- Data science
- Data engineering
- ML and AI engineering
Is the team measurably better at its own work with AI in the loop?
- Engineering
- Analysts
- Product management
- Operations
One cycle, on the work the team already ships.
- Runs per track: build the AI, or work with AI natively.
↺ The next baseline shows the movement.
Everything the cycle produces stays inside.
Where every person and every team stands, with the evidence behind each rating. A verified profile per person, a gap report per team.
The practices applied on the team's own live work, so the change shows up in what they ship.
The choices made during the engagement, written down so the next piece of work starts from them.
The environment the team works inside and the standards encoded as skills. Built once, reused across projects.
- pipeline · fixed, every run goes through it
- spec gate · iteration starts from a spec
- ledger · every experiment recorded
- skills · standards your tools execute
Your team delivers the work while Cadra builds the capability around it.
Built to survive an audit.
- Evidence behind every score
- Every line traceable
- Can be challenged
Where teams use this.
Report capability movement per dimension instead of completion counts.
A large FMCG: function-specific training across six functions, pilot first.
Baseline the bench against a client JD before names go forward.
An IT services firm: AI QA bench measured against a live client JD.
Baseline internal candidates before opening the external req; whoever is close gets a named, trainable gap.
Baseline one team and see exactly where it stands.
One short call: which team to start with, what the simulation looks like, and what the gap report tells you. Everything after the baseline is your call.