NOFire AI

What NOFire AI does

NOFire AI is an AI SRE platform that prevents change-driven incidents before they reach production and resolves existing ones in minutes with high accuracy. It connects changes, services, behavior, and outcomes through a live, time-versioned Production Context Graph. This graph maps services, dependencies, and how they affect each other, allowing for prevention of risky changes, rapid root cause analysis, and continuous learning from every investigation and interaction. The platform gates every agent action at runtime against policies and provides a tamper-evident audit trail.

Products

• Production Context GraphA live, time-versioned model of production that maps every service, dependency, and owner into one live graph. It gathers context from across the stack (deploys, config changes, agent actions, alerts across cloud, CI, services, observability) and keeps a history of every change, allowing reconstruction of any moment in production.
• Context & Control ModelThe core model that captures how services, deploys, dependencies, and incidents connect across time. It scores each change against the real topology before it ships, surfaces blast radius, gates every agent action at runtime against policies, and retains full context of past incidents for lasting protection.
• NOFire AgentsAgents that investigate and prevent incidents. They test multiple hypotheses in parallel, verify with evidence from infrastructure, code, telemetry, and change history, and perform pre-deploy blast-radius analysis and policy gates on every PR. Users can also bring their own agents via MCP.
• Action GatewayA runtime policy enforcement point that checks every action, human or agent, against the live production model and policy before it touches production. It provides a tamper-evident audit trail to SIEMs.
• NOFire AI EdgeA lightweight Kubernetes agent that starts the Production Context Graph by watching clusters, discovering service relationships through DNS traffic, and tracking deployments, scaling events, and config changes.

Key capabilities

• Live production graph
• Runtime agent action gating
• Incident memory and learning
• Continuous time-versioned history of production graph
• Read-only by default deployment
• SaaS or in-VPC deployment options
• PII redaction
• Bring your own model (Bedrock, Azure OpenAI, Vertex)
• Agent enforcement & policy binding
• Advanced analytics and reporting
• Audit logs and compliance reports
• Custom integrations
• Automated investigation and root cause analysis
• Pre-deploy blast-radius analysis
• Policy gates on pull requests
• Support for custom agents
• Runbooks (scheduled, event-triggered, on-demand)
• Tamper-evident audit trail to SIEM
• Role-based access control (RBAC)
• SSO / SAML authentication
• Policy-bound action with OPA-style policies
• Session controls
• Tenant isolation

Use cases

• Preventing change-driven incidents before they reach production
• Resolving incidents quickly with high accuracy
• Understanding and operating complex systems
• Shifting reliability left in engineering teams
• Scoring every change against real topology before it ships
• Surfacing exact blast radius of changes
• Gating autonomous agent actions against policy at runtime
• Retaining full context of past incidents for lasting protection
• Reconstructing any moment in production for investigations
• Deploy risk scans
• Root-cause traces
• Change management checks
• Cost queries
• Compliance checks
• Testing multiple hypotheses in parallel for incident investigation
• Pre-deploy blast-radius analysis
• Policy gates on every PR
• Weekly drift checks
• Monday health scans
• Post-deploy validation
• Event-triggered runbooks (e.g., Grafana alert, PR merged, Slack mention)
• On-demand runbooks via slash commands
• Governing AI agents in production
• Ensuring compliance with regulations like NYDFS Part 500 and EU AI Act
• Automating alert triage and investigation
• Reducing Mean Time To Resolution (MTTR)
• Identifying and addressing alert configuration debt
• Diagnosing database performance issues (e.g., missing indexes, slow queries)

AI approach

NOFire AI uses a proprietary 'Context & Control Model' to build a live production graph, mapping services, dependencies, and changes. It uses AI agents for incident prevention and root cause analysis, testing hypotheses against real evidence. The system is causal, not probabilistic, and continuously updated. It can integrate with frontier models and cloud providers' AI services (Bedrock, Azure OpenAI, Vertex) but states that customer data is never used to train cross-customer systems.

Tech named: Agentic AI, Causal AI, AWS Bedrock, Azure OpenAI, Google Vertex, OpenAI

Industries served

• Software Development
• Online Learning Platforms
• Home Services Technology

What it says sets it apart

• One model covering every change
• Context, Control, and Memory run as one model over a live production graph
• 89% root-cause accuracy on the AI SRE Benchmark
• Connects your stack, builds a live production graph, and gates every agent action at runtime
• No agents to deploy, no code to change for initial connection
• Runs read-only by default, in your VPC, on your own model
• Causal, not probabilistic, model of production
• Live, not snapshotted, model of production
• Every action by a human or agent evaluated against the Context & Control Model before touching production
• Tests multiple hypotheses in parallel with real evidence
• One execution path for every actor (human, CI, agent) through the same gate
• Works with frontier models and cloud providers customers already run (AWS Bedrock, Azure OpenAI, Google Vertex, OpenAI)
• Data never leaves customer environment (for BYOC deployment)
• Causal production graph mapping actual cause-and-effect relationships
• Accumulates operational knowledge from investigations, change history, and conversations
• Gates every agent action at runtime against live production model and policy
• Tamper-evident audit trail to SIEM for compliance
• Read-only access, sovereign by design (customer data processed inside VPC, model never leaves control plane)
• SOC 2 Type II ready, GDPR & UK GDPR compliant, HIPAA aligned
• PII redaction before any model call
• No model training on customer data for cross-customer systems
• Tenant isolation with per-tenant Context & Control Model
• Scores every change against a live production graph and maps blast radius while still a pull request
• Provides specialist explanations with citations for risk findings, not a black-box score

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.