In production
Applied AI in Production
Retrieval, document intelligence, monitoring, and translation systems already running for real clients.
The Stack Behind Every Engagement
Four disciplines, one accountable team - depth where it actually changes the outcome.
AI Systems That Run Themselves, Safely
MLOps, AIOps, and agentic infrastructure - governed and policy-controlled, not just automated.
Most AI initiatives stall between the demo and production because nobody built the operational layer underneath - monitoring, policy control, audit trails, rollback paths. We build that layer first, then the AI on top of it holds up under real use.
Four capabilities, one operational layer
Capabilities
- Models
MLOps & Model Operations
Models taken from notebook to production and kept there - versioning, monitoring, automated retraining.
See how we run models - Infrastructure
AIOps & Infrastructure Automation
AI-driven infrastructure automation with policy control built into every action an agent takes.
See AutonomaOps - Agents
Agent Automation & Governance
Multi-agent systems that execute real workflows, with every action gated and logged.
See how we govern agents
One platform, four systems already running
Where it comes together
See how these capabilities come together in AutonomaOps, our infrastructure automation platform, or explore the applied AI systems we've built for records intelligence, document processing, threat monitoring, and language translation.
Where it comes togetherExplore AI Solutions
Common Questions About Data & AI
FAQ
We build the operational layer AI needs in production - MLOps, AIOps and agent governance - and the applied AI systems that run on top of it.
Usually because nobody built what sits underneath the model: monitoring, policy control, audit trails and rollback paths. We build that layer first so the AI holds up under real use.
AI-driven infrastructure automation. We deliver it through AutonomaOps, with policy control built into every action an agent takes.
MLOps keeps models accurate in production through versioning, monitoring and retraining. Agent governance controls what AI agents are allowed to do, gating each action by policy and logging it as evidence.
Yes - records intelligence, document OCR, threat intelligence and language translation systems are running for clients today.
Bring us the data problem
If you have the data and not the answers - or a model that never made it past the demo - tell us where it stalled.