Cielara provides a live Production World Model that unifies an organization’s code, configuration, infrastructure, policies, and operational intent into a causal graph.
Funding
Funding not disclosed
Founders
Product
Problem
AI-generated code can pass reviews yet behave unpredictably in production, leading to outages and costly incidents. Organizations lack a way to anticipate the impact of code changes across complex configurations, infrastructure, and policies before deployment.
Solution
Cielara builds a live Production World Model that unifies an organization’s code, configuration, infrastructure, policies, and operational intent into a six‑layer causal graph. When a change is proposed, the system simulates it against this exact replica of the production environment, exposing the blast radius, potential failure modes, and downstream impact. The simulation runs automatically within CI pipelines, providing developers and release gates with real‑time risk assessments and root‑cause traces back to the originating commit and approver. Over time, the model learns from each deployment and incident, sharpening its predictions and reducing unnecessary token consumption for AI agents. This enables teams to block hazardous changes before they reach production, improving reliability without slowing development velocity.
Target Audience
Primary users are software architects, developers, and release/ platform engineering teams that manage large, complex enterprise applications and need automated safety checks before code reaches production.
Features
- Six‑layer causal graph (intent, ownership, code, policy, infrastructure, runtime) that represents the full production environment
- Automated pre‑deployment simulation of every code change, showing blast radius and failure scenarios
- CI integration (e.g., GitHub Actions) with dual checks: impact prediction and compliance verification against team‑defined patterns
- Real‑time root‑cause tracing linking failures to commits, pull requests, and approvers
- Continuous learning engine that refines predictions from each deployment, incident, and fix
- 125 M+ token context window storing the entire codebase for efficient, targeted AI navigation
- Built‑in decision record capturing changes, approvals, and constraints for queryable audit trails