Key Takeaways
• Etiq AI has secured €900,000 in seed funding led by GapMinder VC to scale its Data Science Copilot platform.
• The startup focuses on post-training debugging and testing for AI/ML models, targeting speed, fairness, and reliability.
• The Data Science Copilot launched in March 2025 and is designed to shorten debugging time from weeks to minutes.
• Funding will be used to expand team capacity, improve large language model (LLM) capabilities, and accelerate commercial growth.
Etiq AI, a London-based artificial intelligence startup, has announced the successful completion of a €900,000 seed funding round, led by GapMinder VC.
This latest investment adds to £1 million previously awarded through a grant by InnovateUK, and is aimed at accelerating the development and commercialization of Etiq’s flagship product—the Data Science Copilot.
Founded by Iris Anson (CEO) and Raluca Crisan (CTO), Etiq AI is focused on solving one of the most persistent challenges in applied machine learning: the complexity of debugging, validating, and operationalizing AI systems after training.
The company launched its platform in March 2025 to address these issues directly.
“We’re thrilled to announce the successful completion of our latest funding round and to officially welcome GapMinder VC on board as a key partner in our journey. This milestone marks a significant step forward for our team and product, and we’re incredibly excited about the road ahead.”—Iris Anson, CEO, Etiq AI
Inside the Data Science Copilot: AI Testing Reimagined
Etiq AI’s Data Science Copilot is designed to radically reduce the time and complexity involved in debugging machine learning pipelines.
The platform enables data science teams to identify model issues in real time and fix them with precision—without needing to write additional code from scratch or perform extensive manual reviews.
• The platform performs root cause analysis and pinpoints errors within ML code.
• It recommends targeted tests to improve robustness and fairness.
• AI agents suggest solutions based on real-time analysis of data pipeline behaviors.
The tool aims to improve the trustworthiness of AI systems by surfacing hidden biases, flagging edge cases, and enhancing model accountability—a growing concern in both commercial and regulatory environments.
Growing Market, Growing Need
Etiq AI enters the landscape as demand grows for transparent and accountable AI tooling. Analysts estimate that data science platform revenues could exceed $320 billion globally by 2026, with responsible infrastructure solutions expected to become a core investment area for AI-driven organizations.
While many companies focus on pre-training development, Etiq distinguishes itself by prioritizing the post-training phase—the period when AI models are validated, monitored, and pushed into production.
“With the surge in AI agent usage and machine learning-driven applications, there’s an urgent need for robust tools that help developers monitor, debug, and improve model behavior in real time. Etiq’s technology does exactly that, empowering teams to surface hidden biases, identify edge cases, and continuously improve ML performance. The founders bring a rare combination of technical depth and product vision, and we have strong conviction in their vision to become a category-defining company in the AI tooling space.”—Robert Herscovici, Investment Director, GapMinder VC
Roadmap: Feature Expansion and Commercial Rollout
Etiq will use the new funding to enhance the feature set of the Data Science Copilot, particularly with regard to large language model (LLM) capabilities, and to grow its technical and marketing teams.
• Development will focus on LLM-specific debugging and compliance tools.
• The team will scale to support enterprise-grade commercial deployment.
• Marketing initiatives will target adoption among enterprise data science teams.
The company’s vision is to build a foundational AI infrastructure layer that enables organizations to responsibly scale their AI applications while reducing risk, improving model outcomes, and streamlining workflows.
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