Sequential decision-making problems — pricing, inventory, recommendation ordering, control — where the right action depends on the one before it.
Some decisions aren't one-shot predictions — they're a sequence, where today's action changes the state you're deciding from tomorrow. That's a reinforcement learning problem, not a supervised one.
The hard part is almost never the algorithm. It's reward design — specifying what "good" means precisely enough that the trained policy doesn't find a technically-correct but useless way to maximise it — and building a simulation environment realistic enough that a policy trained in it transfers to the real system.
Where live exploration carries real cost or risk, we favour offline RL and off-policy evaluation: learning from historical decision data and rigorously estimating a new policy's performance before it's ever deployed, rather than learning by trial and error in production.
Deployment is staged and monitored — a policy earns expanded autonomy as its real-world performance validates the offline estimates, not before.
How we approach it
Defining state, action space, and — critically — a reward function that actually encodes the business objective, stress-tested for ways a policy could exploit it to score well without doing the right thing.
Building or calibrating a simulation environment realistic enough that policies trained in it hold up in the real system — or, where a simulator isn't feasible, working directly from historical decision logs.
Selecting an algorithm matched to the action space (discrete or continuous), data availability, and whether online exploration is safe — online RL where it is, offline RL from logged data where it isn't.
Estimating how a new policy would perform against the real objective before it ever touches production, using the historical data honestly — with confidence bounds, not a single optimistic number.
Rolling out with guardrails and a fallback policy, expanding the trained policy's autonomy only as live results confirm the offline estimates — and monitoring for reward hacking after launch, not just before it.
What the loop looks like
Agent, action, environment, reward
The agent acts, the environment responds — the policy improves from the reward signal, one cycle at a time.
Tell us about the problem and we'll scope it together — no obligation.
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