About Ellie.ai #
Ellie.ai is a Helsinki-based enterprise data modeling platform that serves 50+ major enterprise customers across healthcare, telecommunications, energy, and IT sectors. Founded in 2018 with €2.5 million in seed funding from top-tier European VCs Newion and Crowberry Capital, Ellie has grown to a team of 20+ experts serving customers worldwide from Canada to New Zealand.
The company's AI-powered platform helps enterprises streamline data modeling and claims to help customers achieve analytics KPIs up to 60% faster. With integrations to industry-standard tools like dbt and GitHub, Ellie.ai represents the cutting edge of enterprise data product design.
Key Players: Mikael Lavi (Software Architect) and Jari "Jaffa" Jaanto (CTO), leading a sophisticated development team working on complex AI and data modeling challenges.
The Challenge: Scaling AI Development Consistency #
Even at a well-funded, technically sophisticated company like Ellie.ai, the team faced a common problem with AI-assisted development. While all developers were using Cursor for AI code generation, each team member was working with individual context setups and rules.
"We were all using Cursor, but everyone had their own way of setting it up," explained Mikael, the company's Software Architect, during our consultation call. "There wasn't much consistency in how the AI understood our architecture."
For an enterprise platform handling complex data modeling workflows for major corporations, this inconsistency posed real risks. Different developers would get different AI suggestions even when working on the same critical systems, potentially leading to architectural drift and integration problems.
The Solution: Enterprise-Grade AI Context Management #
Working with Ellie.ai's technical leadership, we implemented a systematic approach to create shared AI context across their development team. Rather than each developer maintaining individual Cursor configurations, we established a unified system that could scale across their growing engineering organization.
The Process:
Mikael used our platform to analyze Ellie's sophisticated codebase architecture - encompassing their frontend applications, backend services, APIs, and data processing systems. The tool generated comprehensive architectural descriptions and context rules that could be shared across the entire development team.
The generated context included not just code descriptions, but also the metadata and file patterns that Cursor uses for activation, ensuring the AI would engage appropriately based on the specific components developers were working on.
Implementation at Enterprise Scale #
Rather than working in isolation, Ellie.ai's team took an enterprise approach to rollout:
Management: Mikael runs the analysis periodically to update their shared context, reviews the output for accuracy, then publishes the updated rules to their main repository.
Standardization: All developers now work with the same AI understanding of their codebase architecture, eliminating the previous fragmentation.
Continuous Improvement: The system integrates with their existing development workflow, allowing for regular updates as their platform evolves.
Results at a Leading AI Company #
For a company at the forefront of AI-powered enterprise tools, the improvements were immediately noticeable:
- Developers started getting consistent AI assistance, particularly valuable when working on Ellie's complex data modeling and analytics features.
- The AI became significantly more aware of their codebase structure, especially helpful for a platform that processes enterprise data at scale.
- As Mikael noted, "It's also about people learning to use Cursor much more" - the shared context helped the entire team become more effective with AI tools.
Strategic Impact #
For Ellie.ai, this wasn't just about individual developer productivity - it was about maintaining the code quality and architectural consistency.
The shared AI context approach has become part of their development infrastructure, supporting their mission to help major corporations get 60% faster analytics results through better data modeling tools.
The Enterprise Takeaway #
This case study demonstrates how well-funded, technically sophisticated companies benefit from systematic approaches to AI development tool management. The solution scales from individual developers to entire engineering organizations.
For other enterprise software companies, particularly those in the AI and data space, having consistent AI-assisted development practices isn't just about productivity - it's about maintaining the architectural integrity that enterprise customers demand.
Ellie.ai continues to evolve their AI development practices as they scale their platform serving enterprise customers across multiple continents. Their thoughtful approach to AI tool management reflects the same attention to systematic solutions that makes their data modeling platform successful with major corporations.