Reinforcement learning
Reinforcement learning, applied where feedback exists.
We build systems that get better every time they run.
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What we do
Agent improvement
Agent behaviour tuned against measured outcomes, not anecdotes.
Ranking and routing
Orderings that learn from what people actually chose.
Operational policies
Scheduling, pricing, and allocation decisions that compound.
Evaluation loops
The feedback signal is the asset. We build that first.
How we do it
- 1
Find the loop
A repeating decision with a measurable outcome.
- 2
Bench offline
A cheap simulation before production.
- 3
Audit the reward
Test what the system is actually optimising.
- 4
Roll out capped
Shadow mode first, full control last.
Most RL projects fail before the algorithm matters.
Simulate first
A cheap offline bench before anything touches production.
Reward audits
A mis-specified reward gets optimised into a disaster. We test the reward itself.
Baseline gates
RL ships only when it beats the boring policy it replaces.
Small blast radius
Shadow mode and capped rollouts before full control.
What you'll achieve
Decisions that compound
Scheduling, pricing, routing, and allocation improve with every cycle instead of staying frozen.
Proof before production
The offline bench shows the gain before anything touches live traffic.
A margin you can see
The shipped policy is measured against the one it replaced, so the gain is a number rather than a feeling.
Common questions
Is RL overkill for our problem?
Often, and we will say so. Supervised learning or a well-designed heuristic beats RL in most business settings. RL earns its place when decisions repeat at volume and feedback is fast and measurable.
What do you need from us to start?
A decision that repeats, a measurable outcome, and logs. If those exist, an offline bench can be running within weeks.
Do you do RLHF?
We build the feedback and evaluation loops that RLHF-style training depends on, and preference collection where a product needs it.
Tell us what you need built.
A 20 minute call is enough to work out whether a pod fits. The first week of work is defined on that call.