Applied AI Lab
Building models, evaluation systems, and agent architectures required for AI to reason about and operate complex production environments.
Hero image — replace with team photo
Our Mission
The next frontier is not writing software, but operating it. Our mission is to enable AI systems that safely and reliably operate the worlds production software, freeing engineers to innovate more.
Why this problem is hard
Fragmented signals
Production systems are dynamic, distributed, and stateful. Failures emerge across services, dependencies, and time, not in isolated tasks.
Complex reasoning
General-purpose AI is built for breadth. Production operations require domain-specific reasoning, high reliability, and the ability to operate under uncertainty.
Operational reliability
Reliable AI in production depends on the full stack: models, evaluation, environments, and orchestration working together.
A different approach to AI for production
Production is different
Production systems are dynamic, distributed, and stateful. They are not static datasets or generic software tasks.
General-purpose AI is not enough
Production operations require domain-specific reasoning, reliability, and the ability to operate under uncertainty.
The system matters as much as the model
Reliable AI in production depends on models, evaluation, environments, and orchestration working together.
Research focus
Domain-specific models
Post-trained models designed for reasoning across production telemetry, long-horizon workflows, and system-level behavior.
Evaluation systems
Systems for evaluating correctness, reasoning quality, and reliability in production workflows without clean ground truth.
Simulated environments
Execution frameworks for tool use, guided remediation, approvals, and safe action under operational constraints.
Agentic systems and control
Multi-agent systems that investigate, diagnose, and coordinate across distributed production environments.
How these systems work
AI systems operating production environments must coordinate across real-time telemetry, system topology, and specialized agents.
Model layer
Post-trained open-source and custom models best-in-class at production tasks
Agent layer
Detection and diagnosis of issues across distributed systems
Action layer
Automated tool usage plus guided remediation with approvals and safe execution
Context layer
Knowledge management and continual learning from every interaction
User layer
One workspace abstracting production complexity across code, infra, telemetry, and knowledge
Where this is going
The shift is from human-in-the-loop to human-on-the-loop: humans define policy, guardrails, and exceptions while AI systems execute within those boundaries.
Assist
AI supports investigation. Engineers stay in the drivers seat while AI surfaces context, correlates signals, and suggests next steps.
Approve
AI proposes actions. It drafts remediation plans, suggests changes, and presents options for human review before execution.
Operate
AI acts within guardrails. Autonomous operation for defined scenarios, with humans setting policy and handling exceptions.
The people of the lab
Built by researchers and engineers from leading AI labs, working alongside teams operating some of the worlds most complex production systems.
Research
Researchers from Meta Superintelligence Labs, Google Deep Research and others, advancing domain-specific models, evaluation systems, and simulated environments for AI in production.
Engineering
Engineers with deep observability and infrastructure domain expertise are building the foundation and systems required for reliable AI operation in complex production environments.
Collaboration
Partnering with transformative teams operating complex and dynamic production systems, grounding the labs work in real-world complexity.
