We build reinforcement learning environments and train compact language models for your business tasks. From experiment design to deployment, with your data and your team.
An AI lab dedicated to your context, from environment to model.
The challengeLearn to allocate requests across teams within capacity and deadline constraints.
The agent observes the request and each team’s capacity.
Simplified example: +1 for allocating with capacity; −1 for choosing a full team.
Two specialties. From applied research to production.
RL environments and Small Language Model (SLM) projects, delivered independently or combined to fit the challenge. Each workstream has defined deliverables and evaluation criteria.
We turn your business rules, data, and interactions into reinforcement learning environments. We define observations, actions, rewards, and scenarios to train and evaluate agents or policies.
We select and specialize smaller language models for your domain. Through curation, fine-tuning, and distillation, we target the right balance of quality, cost, and latency for your tasks.
Curation of examples, trajectories, and quality criteria for each experiment.
Baselines, test scenarios, and reports to decide what goes into production.
Training and inference pipelines sized for your environment.
Models and policies connected to agents, tools, and internal systems.
Together, we define what the environment or model needs to do, how to measure the result, and when it is ready for deployment.
Map data, constraints, and hardware. The current solution becomes the reference for evaluating experiments.
Design RL scenarios and rewards, or curate training and test examples for the SLM.
Compare versions, analyze failures, and measure quality, cost, and latency against agreed criteria.
Integrate the solution, monitor behavior, and plan new cycles based on the results.
Deliverables, responsibilities, and ongoing support are agreed in the project scope.
We design deployment around your requirements: private cloud, on-premise, or isolated environments. Integrations, data access, and model selection are part of the architecture from the start.
Our infrastructure tracks traces, evaluations, and model versions. It supports experiment comparison and failure analysis throughout RL and SLM projects.
Explore the infrastructureA training environment with observations, actions, rewards, and scenarios defined for the task. The project can include policy training, evaluation, and integration with your systems, depending on the scope.
A compact language model, selected and specialized for tasks in your domain. We define the model, dataset, and fine-tuning or distillation strategy around your quality, latency, cost, and hardware requirements.
No. These are two workstreams that can operate independently. We can build an RL environment, specialize an SLM, or combine both approaches when the use case calls for it.
We discuss the task, available data, and infrastructure. Then we define the scope, baseline, deliverables, and acceptance criteria. Agents and integrations are included when they are part of the application.
An environment for learning to decide. A model for mastering a task. Bring your context and let’s define the project.
RL environments and SLMs, from research to deployment.