Launching Resolve AI Labs backed by new $40M Series A Extension
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.
Built by researchers and engineers from leading AI labs, working alongside teams operating some of the worlds most complex production systems.
Researchers from Meta Superintelligence Labs, Google Deep Research and others, advancing domain-specific models, evaluation systems, and simulated environments for AI in production.
Engineers with deep observability and infrastructure domain expertise are building the foundation and systems required for reliable AI operation in complex production environments.
Partners from transformative teams operating complex and dynamic production systems, grounding the labs' work in real-world complexity.
Production systems are dynamic, distributed, and stateful. Failures emerge across services, dependencies, and time, not in isolated tasks.
Resolving issues to optimization, spans thousands of services, petabytes of telemetry, and deep dependency chains.
Agents must investigate over extended periods, share evidence, and cross-validate hypotheses - this is beyond any single-turn reasoning.
The shift is from AI-assisted to human-on-the-loop: humans define policy, guardrails, and exceptions while AI systems execute within those boundaries.
AI supports investigation. Engineers stay in the drivers seat while AI surfaces context, correlates signals, and suggests next steps.
AI proposes actions. It drafts remediation plans, suggests changes, and presents options for human-in-the-loop (HITL) review before execution.
AI acts within guardrails. Autonomous operation for defined scenarios, with humans-on-the-loop (HOTL) setting policy and handling exceptions.
Building models, evaluation systems, and agent architectures required for AI to reason about and operate complex production environments.
Training models that are best-in-class at production tasks
Systems for evaluating correctness, reasoning quality, and reliability in production workflows without clean ground truth.
Execution frameworks for tool use, guided remediation, approvals, and safe action under operational constraints.
Multi-agent systems that investigate, diagnose, and coordinate across distributed production environments.
