RL environments
Purpose-built worlds with the states, actions and rewards your research requires.
Applied AI Lab
Build the world your models need.
Commissioned RL environments, evaluations and applied AI systems for consequential problems.
Purpose-built where the problem demands it.
One lab across the full system, from the world a model learns in to the evidence that proves it works.
Evaluations
Behavior measured against clear tasks, boundaries and failure modes.
AI agents
Agents engineered around the environment, tools and operating constraints.
Simulation and forecasting
Systems for exploring likely futures before acting in the real one.
Data infrastructure
The pipelines and alignment work that make reliable models possible.
A commissioned system, without the handoff gaps.
The same small team stays close from specification through integration.
Define
The behavior or outcome that matters.
Specify
The world, data and measures.
Build
The environment, agent or system.
Validate
The behavior under real constraints.
Deliver
A working system integrated with your stack.
Erasteel
From fragmentedoperations data toone deterministic workflow.
SAP, historian and operator logs aligned into one analytics system for forecasting, scenario simulation and sulphur risk modelling.
Ingest
SAP, historian and operator logs
Align
Deterministic time index
Model
X / P / Y partitions with GRU, LSTM and Transformer work
Decide
Forecasting, scenario simulation and sulphur risk
Built for operational reality
Leakage-free validation
Autoregressive scenario simulation
Bayesian-network risk modelling
View methodology
Leakage-free sequence validation, autoregressive scenario simulators and Bayesian-network risk modelling were applied to aligned operational data.
