From “prompt-and-pray” to production-ready: Straion raises €1.1M to bring governance to AI coding at scale

The software industry has entered what many call the “prompt-and-pray” era of development. Tools like GitHub Copilot and Cursor allow developers to generate code at high speed. However, for engineering leaders managing large teams, this rapid output often creates new challenges. While more code is being produced than ever before, it may lack the structure, compliance, and internal standards required in complex enterprise environments.

One of the biggest issues with AI-first coding is the need for constant manual correction. An AI tool might generate code that works technically, but it may not follow a company’s internal naming conventions, data protection rules, or architectural standards. As a result, senior engineers often spend significant time reviewing and correcting AI-generated work to prevent long-term issues.

Read Also - Tewke raises £1.5M to expand its AI-driven home energy platform

Straion addresses this problem by giving teams a central hub to define and manage engineering rules. It can dynamically apply the right standards to each task and validate development plans before code is implemented — not just after it is written. The platform integrates smoothly with existing tools such as Claude Code, Cursor, and Copilot.

The company’s mission is clear: help teams move faster, reduce inconsistencies, and increase confidence in the reliability and quality of AI-generated code.

"The industry has spent the last two years obsessed with making AI faster. But in an enterprise environment, speed without alignment is a liability," says Lukas Holzer, co-founder of Straion.

"We built Straion to give AI the organisational context it was missing—moving it from a trial-and-error tool to a precision instrument that understands how your company actually builds software."

Straion addresses this challenge by turning static documentation into active, machine-readable guardrails. Instead of waiting for mistakes to happen, the platform provides AI tools with the right context at the exact moment it’s needed. Most importantly, it checks and validates the AI’s plan before any code is implemented.

This approach changes the workflow from reactive clean-up to proactive precision. By using machine learning to dynamically retrieve only the rules relevant to a specific task, Straion enables governed autonomy in AI-driven development. In simple terms, it gives AI systems the guidance and structure they were missing.

According to Marathon VC, understanding why Straion matters requires looking at its origins in Linz, Austria. The founders are not trend-following developers, but experienced enterprise software operators.

Lukas Holzer, Fabian Friedl, and Katrin Freihofner previously worked together at Dynatrace, a global observability company. During their time there, they saw a recurring problem: as organisations grew, the “invisible rules” — such as architectural standards, security policies, and naming conventions — became harder to enforce. These rules were often buried in outdated documentation, like long Confluence pages or lengthy PDFs that were rarely updated.

When AI tools began generating code at very high speeds, this documentation gap became even more serious. While AI could quickly write functional code, it lacked awareness of how that code fit within the company’s broader systems, standards, and architecture.

"Most investors are looking for the next AI code generator. We were looking for the guardrails," says Panos Papadopoulos, Partner at Marathon VC.

"Lukas, Fabian, and Katrin aren't just building a tool; they are building the governance layer that makes the autonomous future possible for the enterprise.

They have the technical pedigree from Dynatrace to solve what we believe is the most critical bottleneck in modern engineering. We are proud to back this team as they build the governance layer that will define the next decade of software engineering."

The funding will be used to focus on three key areas: strengthening the product’s rule governance and plan-stage validation features, expanding integrations to support large-scale engineering workflows, and hiring mission-driven talent across AI engineering and full-stack development.