01
An output you keep
Each step ends with something concrete: a framing doc, a tested environment, policies, a report, a running system.
Process
Five steps. Clear outputs.
Every engagement follows the same arc. Each step has a clear output, so you always know where the work stands.
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Every engagement is an episode: we observe, act, measure the reward, and improve the policy, until the system behaves the way you intended.
04 / Process
From problem to deployed policy.
Every engagement follows the same arc. Each step has a clear output, so you always know where the work stands.
Step 01 / Problem framing & feasibility
Discover
We map the decision you want automated, the data you have, and the constraints that cannot move.
Step 02 / Tested environment
Model the environment
We build a simulator or digital twin with the right state, actions, and dynamics, then validate it against reality.
Step 03 / Candidate policies
Train and iterate
Reward design, algorithm selection, and fast training loops. We iterate until behaviour matches intent.
Step 04 / Evaluation report
Evaluate
Held-out scenarios, stress tests, and adversarial cases. Nothing ships on a single good curve.
Step 05 / Production system
Deploy and monitor
We ship behind the guardrails, monitor drift, and set up retraining so the system keeps learning.
Every step
What never changes.
02
Evaluation before release
Nothing ships on a single good curve. Held-out scenarios and stress tests come before deployment.
03
Your code, your infra
Everything lives in your repositories and cloud accounts, with documentation and a handover.