Build, ship, run
AI in production.
A platform for production AI — data, models, agents, and ops — engineered for governance, observability, and reuse across the enterprise.
What’s inside
The pieces that make ai factory work.
AI
LLM-powered solutions — RAG, summarisation, copilots — grounded in your data.
ML
Classical ML for forecasting, classification, anomaly detection — productionised.
AIOps
AI/ML applied to operations — anomaly detection, noise reduction, correlation.
Agentic AI
Autonomous agents that observe, reason, plan, and act — within governance.
AI Models
Model lifecycle — training, deployment, monitoring, retraining, registry.
AI that ships, not AI that demos.
A factory for AI — paved roads from data to production, with observability and governance baked in.
- Paved-road MLOps
- Model registry + lineage
- Production observability
Agentic AI, with the safety on.
Agents grounded in the Context Model, with policy gates, audit trails, and human-in-the-loop — built for regulated environments.
- Context-grounded agents
- Policy gates
- Full decision lineage
AIOps in the operational loop.
Anomaly detection, noise reduction, and correlation working against the operational graph — so signals become actions.
- Anomaly detection
- Noise reduction
- Correlation engines
Use cases
Where teams put this to work.
Enterprise copilot
A grounded, governed copilot for your teams.
Agentic operations
Autonomous agents embedded in operational workflows.
AIOps for IT/Ops
AI/ML applied to operational signals at scale.
Continue exploring
Other solutions, same outcome.
Ready to work on ai factory?
Bring us your environment, your constraints, your goals — we’ll bring the senior people who’ve solved this before.