NOFire provides an AI‑driven platform that builds a live production graph by aggregating signals from deployments, configuration changes, agents, and observability tools. It scores each change against the real topology, gates autonomous actions against policy, and retains incident context to prevent repeat failures, enabling teams to ship faster with continuous safety checks.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations struggle to maintain an accurate, real‑time view of their production environment, making it difficult to predict the impact of code changes, enforce policy compliance, and quickly resolve incidents. Disconnected signals from CI/CD, observability tools, and service catalogs lead to fragmented alerts and repeated root‑cause analysis.
Solution
NOFire delivers an AI‑driven platform that continuously builds a live production graph linking services, dependencies, owners, and change history. Each proposed change is scored against this graph to reveal its blast radius before deployment, and autonomous agent actions are gated by configurable policies at runtime. The system retains full context of past incidents, attaching prior root‑cause analyses to similar future changes, effectively providing a “time‑machine” for production. All data is collected from cloud providers, CI pipelines, telemetry, and observability sources, unified into a single model that powers investigations, automated runbooks, and compliance checks. The platform runs read‑only by default within the customer’s VPC, ensuring data stays on‑premises while offering optional custom agents and integrations.
Target Audience
Primary customers are SRE, DevOps, and platform engineering teams that manage complex, multi‑cloud production environments and need automated risk assessment, policy enforcement, and incident knowledge reuse.
Features
- Live production graph that maps every service, dependency, owner, and change event across cloud, Kubernetes, databases, and observability tools
- Pre‑deployment blast‑radius scoring that surfaces the exact impact of a change before it ships
- Runtime policy enforcement that gates autonomous agent actions and blocks violations in real time
- Incident memory that stores full context of past incidents and automatically links prior root‑cause analyses to new, similar changes
- AI‑powered investigations that query the unified model across signals, generate causal chains, and suggest remediation actions
- Extensible agent framework (MCP) allowing customers to bring their own agents that inherit the same context, policy gates, and audit trail
- Integration with 20+ tools (e.g., GitHub, GitLab, Datadog, Prometheus, Loki, Slack, PagerDuty) and support for major cloud providers (AWS, Azure, GCP)
- Deployment options include read‑only SaaS mode or BYOC within the customer’s VPC, with micro‑VM isolation for secure execution