NOFire AI Launches the Context & Control Model for Production AI, Raises $2.5M Seed from Marathon Venture Capital

NOFire AI today launched the Context and Control Model for Production (CCM), the governance model for AI agents acting in production, and announced a $2.5M seed round led by Marathon Venture Capital, with participation from a16z venture scout fund.

As engineering teams deploy AI agents to act directly in production (investigating incidents, executing remediations, running playbooks), they face a critical challenge: those agents have no model of how production actually behaves. The result: agents act on partial context, the same failures repeat, and the trust required for autonomous action never gets built.

The CCM closes that gap. Built on dependency data and production knowledge from every service, deployment, configuration, and incident across the engineering organisation, it gives production AI the context to investigate correctly and act safely, with governance over every action before it executes.

The production context imperative

With the launch of the CCM, NOFire AI is defining a new critical layer in the engineering technology stack: the production context layer. It unifies dependency data, production knowledge, and causal reasoning to ground production AI in how its environment actually behaves, and to govern its every action, deepening continuously as the system learns from investigations and outcomes across the team.

From the founders

“The blocker to AI autonomy in production is not AI capability anymore. It is production context, and the controls that go with it. The CCM is purpose-built for production, not bolted onto observability.”

Spiros Economakis, Co-founder, CEO

“Nine years building observability products convinced me that AI agents need a proper model of production and a runtime that gates every action against it. We built both as one system.”

Panagiotis Moustafellos, Co-founder, CTO

“Bounding what an autonomous agent can do is not a policy problem. It is an execution-environment problem. Control has to happen at the layer where actions execute. That is what we built.”

Anastasios Nanos, Co-founder, Chief Scientist

From the investor

“Every enterprise runs production too complex for any team to fully understand, and the move to AI agents makes that problem existential. NOFire gives agents the context they need and the guardrails enterprises require.”

Panos Papadopoulos, Partner, Marathon VC

The CCM powers trusted production AI

At the foundation of the CCM is the Production Context Graph: a live, time-versioned map of every service, dependency, deployment, configuration change, and failure pattern, reconstructable at any point in time. Every investigation deepens it. A team that starts with NOFire today inherits everything the system has already learned about their stack; a team starting six months later begins from zero. That gap compounds.

The CCM delivers four capabilities across the full production lifecycle:

• Prevent. Every code and infrastructure change is scored against the graph before it deploys. Blast radius mapped. Risk flagged. Rollout strategy recommended. Changes that would have caused incidents are caught before they reach production.
• Resolve. When something breaks, NOFire traces the causal chain from change to consequence through the actual dependency graph, not a statistical correlation from metrics. Root cause in minutes, not hours.
• Memory. Every investigation feeds back into the model. NOFire captures confirmed root causes, scans past postmortem channels, and builds a permanent knowledge library from what the team has already learned. When an on-call engineer corrects the system at 2am, the next person on rotation inherits that correction.
• Workflows. Turn the team's best playbooks into proactive workflows triggered by an alert, a merged pull request, or a slash command. Every execution logged: who triggered it, what ran, what it returned.

Sovereignty by architecture

The CCM runs entirely on the customer's own infrastructure, using models they already control: AWS Bedrock, Azure OpenAI, Google Vertex. Data never leaves their environment. Their IAM, their security controls, their compliance policies already in place.

The future of production is autonomous and governed

NOFire views the CCM as the foundational step toward a new operating model for engineering teams. In that future, AI agents write the code, understand the production environment they are shipping into, and act within governance boundaries shaped by years of production experience. Engineers define what the system can do, and stay accountable for the outcomes.

Every operations team will need this layer, not as another add-on to observability, but as the governance model production AI requires to operate safely.