Operations & Logistics
Routing, scheduling, predictive maintenance.
Lunar · Enterprise AI Lab
RL environments.
Small language models.
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.
RL environmentsSmall Language ModelsDeployment in your infrastructure
RL environments & SLMs
Your training environment. Your specialized model.
RL environments and Small Language Model (SLM) projects, delivered independently or combined to fit the challenge. Each workstream has defined deliverables and evaluation criteria.
RL environments
The right context for learning to decide.
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.
- Versioned environments and training scenarios
- Task-specific rewards and constraints
- Policy evaluation against a baseline
Environment design
Your business states, actions, and scenarios.
We model available observations, permitted actions, and state transitions. Training and test scenarios reflect operational rules and data, including failure cases and constraints.
Reward engineering
A reward that reflects the task.
We define reward signals and penalties to guide learning. We evaluate unwanted behavior and compare policies with existing rules before deployment.
Small Language Models
Compact models. Well-defined tasks.
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.
- Curated datasets and separate test sets
- Model fine-tuning and distillation
- Evaluation and serving on the selected hardware
Fine-tuning and distillation
Specialize the model for your domain.
We curate examples and reference-model outputs to train the SLM. Versions are compared on a separate test set and improvements are validated under the project’s operating conditions.
Evaluation and serving
Quality measured in your environment.
The model needs to meet the task requirements on the available hardware. We evaluate results before integrating the SLM into your systems.
- Task-level quality and failure analysis
- Cost, latency, and memory usage
- Versioning and production monitoring
Owned AI Infrastructure
Your data. Your models. Your servers.
A full AI stack deployed inside your perimeter — gateway, traces, training, serving. Compliance-ready. Open-source. No data leaves your environment.
Full-stack AI inside your perimeter
Your data never leaves your environment.
A complete AI stack — gateway, tracing, training, serving — deployed inside your own infrastructure. On-premise, private cloud, or air-gapped. No outbound data, ever.
- On-premise, private cloud, or air-gapped
- LGPD, GDPR, HIPAA, SOC 2 ready
- Open-source — no vendor lock-in, ever
On-premise or private cloud
Deploy to AWS, GCP, Azure, or bare metal. You choose where the stack lives.
Air-gapped available
For regulated environments with strict network isolation. No outbound traffic.
Open-source, MIT license
Full codebase visibility. Fork it, audit it, extend it. No lock-in.
LGPD · GDPR · HIPAA · SOC 2
Pre-built compliance postures for each framework. Your team stays in control.
Industries
Where RL environments and SLMs fit.
Example tasks to start the conversation. The initial assessment identifies an approach that fits the project’s data and constraints.
Finance & Risk
Pricing, credit scoring, fraud detection.
Healthcare
Triage, decision support, clinical summaries.
Legal
Contracts, compliance, knowledge agents.
Engineering
Coding agents, PR review, build intelligence.
Customer Experience
Support, FAQ, lead qualification.
Data & Analytics
SQL, extraction, executive insights.
Regulated & Government
Air-gapped, redacted, compliance-first.
Services
From dataset to deployment, with your team.
Together, we define what the environment or model needs to do, how to measure the result, and when it is ready for deployment.
Training data
Curation of examples, trajectories, and quality criteria for each experiment.
Reproducible evaluations
Baselines, test scenarios, and reports to decide what goes into production.
Infrastructure and serving
Training and inference pipelines sized for your environment.
Operational integration
Models and policies connected to agents, tools, and internal systems.
What do you need to train?
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.