SuperPlane has raised a $2.6 mn pre-seed round led by Credo Ventures to help engineering teams handle rising volumes of AI-generated software without weakening production controls.
First Momentum Ventures joined the investment, alongside angel backers including Dash0 CEO Mirko Novakovic, Manta founder Tomas Kratky, and Prototype Capital’s Andreas Klinger. Other investors include builders from infrastructure, developer-tools, and AI-native software companies.
SuperPlane plans to use the money for product development, work with design partners and early customers, plus expansion of its open-source ecosystem.
The company is building an open-source automation engine and AI-first control plane for engineers and AI agents working on production infrastructure, and provides a programmable system for operational work after developers write code.
That work includes reviews, testing, deployments, infrastructure orchestration, approvals, incident response, rollback paths, observability feedback, and operating decisions.
Darko Fabijan, Marko Anastasov, Lucas Pinheiro, Aleksandar Mitrovic, Igor Sarcevic, and Petar Perovic founded SuperPlane. Fabijan and Anastasov met at university in 2003 and have built companies together for more than two decades.
Before SuperPlane, they built and scaled Semaphore, an early continuous integration and testing company. Semaphore helped teams adopt container-based infrastructure and grew into a platform processing mn of jobs for organizations including Intuit, Toyota Connected, Confluent, Replit, and Superhuman.
Their experience shaped the team’s view of modern infrastructure operations. They have worked with growing systems, operational workflows under pressure, and teams balancing release speed with production safety.
Darko Fabijan, SuperPlane’s CEO, and CPO Marko Anastasov previously built Semaphore. Engineering teams at Confluent, Replit, and Superhuman used the developer infrastructure company.
AI is changing how software gets built, but the bigger challenge is what happens next. Engineering teams are about to face far more change flowing toward production, and the answer cannot be more manual coordination or more tribal knowledge. The future of operations is supervised automation through systems engineers can trust
Darko Fabijan, SuperPlane’s CEO
“SuperPlane gives humans and agents a shared control plane for production workflows, with the context, guardrails, and deterministic execution needed to operate safely. We are building toward a future where engineering organizations can move at the speed of AI without losing control of the systems they depend on. This funding helps us accelerate that work with our design partners, early customers, and open-source community”, Darko Fabijan said.
Software organizations spent decades operating around one restraint: humans wrote the code. That limit shaped ticket queues, review gates, CI pipelines, release trains, approval chains, deployment runbooks, incident procedures, and on-call rotations.
Teams needed those systems to release software safely when changes moved at human speed. AI coding tools now generate code, modify it, test it, and explain it faster than most organizations review, validate, deploy, observe, or operate resulting systems.
Engineering teams face a constraint limited to writing software. The strain now sits in everything required after code exists, including review, test execution, deployment validation, infrastructure checks, and risk assessment.
A pull request still needs review before release. Teams still need preview environments, tests, infrastructure review, and clear risk assessment before production deployment.
Deployments require approvals, health checks, rollback plans, and communication during execution. Incidents need context, while production systems still depend on people making sound decisions under pressure.
AI has sped up software creation, but it has not improved how organizations operate systems behind it. SuperPlane targets this gap with programmable workflows for humans and agents.
The next stage of engineering depends on whether organizations turn intent into production changes safely. Better coding assistants alone will not settle the issue for infrastructure teams.
Small configuration changes sometimes trigger serious downtime in complex production systems. Institutional knowledge often sits across Slack threads, dashboards, runbooks, informal memory, and senior engineers’ experience.
Fully autonomous AI is insufficient in that setting. Engineering teams need systems where agents contribute without removing human judgment, policy enforcement, visibility, or control.
SuperPlane makes the operational layer programmable for engineering organizations. It coordinates existing tools rather than replacing them with a chat interface.
The platform gives humans and agents a shared control plane for engineering work. Policies, approval paths, deployment patterns, risk checks, incident procedures, and feedback loops become visible, versioned, auditable workflows.
SuperPlane describes this layer as a runtime for software organization operations. The product focuses on structured processes around production infrastructure rather than code generation itself.
“At Credo, we invest in the best teams with a relationship to Central or Eastern Europe, wherever they are in the world. We believe Darko and Marko have seen greatness while building Semaphore and selling its product to the likes of Confluent and Superhuman.
Matěj Míček, Partner at Credo
“We already know for sure that the world of software development will never be the same. We are currently experiencing the shift in how code gets written, but the DevOps layer is yet to change. We are convinced Darko and Marko have been at the state of the art for long enough to make it happen”, Matěj Míček noted.
SuperPlane is an open-source automation engine for event-driven engineering workflows. It sits between code generation and production infrastructure, helping teams structure software delivery work as deterministic workflows with records of each action.
Engineers work through a console and generative interface. AI agents work through APIs, the command line, and Model Context Protocol, with both sides operating under the same policy rules.
SuperPlane connects Git repositories with CI/CD tools and cloud infrastructure. It also links observability platforms, communication services, incident systems, LLMs, and internal services into operational workflow environments.
One SuperPlane App might detect a GitHub pull request, then create an ephemeral AWS preview environment. It may run tests, assess risk, request approval, deploy a change, monitor production health, trigger rollback activity, and return results to engineers or agents preparing the next release.
SuperPlane positions this as more than task scripting. The platform records multi-step operational workflows across their full execution history.
When a deployment fails halfway through, the system records what ran and where it stopped. It identifies the failure point and sets out the next defined action for teams.
Each execution adds information to the organization’s operational memory over time. Automation performs tasks, whereas autonomy depends on context, policy, memory, and defined lines between agents and human decision-makers.
SuperPlane gives engineering teams those defined boundaries. Its model sets expectations around what agents can do and where humans must decide.
SuperPlane includes hundreds of integration components across AWS, GCP, GitHub, GitLab, Slack, PagerDuty, Datadog, OpenAI, Claude, and other platforms. Teams use it for AI-led code review, release management, operational dashboards, incident response, infrastructure orchestration, continuous feedback loops, and production deployment controls.
SuperPlane applies that background to an environment where AI agents and human engineers manage production systems together. The company now has 12 employees focused on ecosystem growth and SuperPlane Cloud.
