Case study
From 2% to 50%:
How we turned AI lab into production powerhouse
The gap between laboratory and production isn't a technological problem - it's an architectural problem of thinking about AI as an experiment, not as a product.
Starting point
The automotive industry is dominated by the belief that AI is the future - every company has hundreds of artificial intelligence initiatives, every team experiments with machine learning, and C-level executives buy out all data science conferences. However, reality brutally verifies these ambitions: in most automotive organizations, only 2% ofAI projects make it to production.
Our client, a leading player in the automotive industry, found themselves at the center of this paradox. They employed world-class data scientists, invested in cutting-edge technologies, and ran hundreds of experiments - but their AI laboratories resembled academic incubators more than business solution factories. Out of 50 AI projects, at most one or two would reach actual users.
From experiment to product: Ecosystem approach
Approach
Instead of building another MLOps platform, we focused on redesigning the entire AI lifecycle - from experiment to production. We created an ecosystem that treats every model not as a one-time experiment, but as a future product with complete infrastructure around it.
Our strategy was built on three pillars:
- Infrastructure-as-code: Every AI experiment automatically receives a production-ready environment
- Compliance-by-design: Security and compliance built in from day one, not added post factum
- Scaling through standardization:Unified frameworks that eliminate 80% of repetitive development work
We also conducted migration from legacy systems, ensuring smooth transition for hundreds of projects without disrupting their development cycle.
Breaking The Linear
We reversed the logic - every experiment from the first line of code runs in a production-grade environment. There's no "gap" between laboratory and production, because the laboratory is already production. Data scientist clicks "deploy" and their model automatically goes to a secure, compliant, scalable production environment.
AI as daily delivery, not R&D project
Summary
We transformed the client from a company that "experiments with AI" into an organization that "producesAI". This is a fundamental shift in thinking - from treating artificial intelligence as expensive R&D to seeing it as core business capability.
We opened up a trajectory for the client that they hadn't seen before: a world where AI isn't an expert in the laboratory, but a living element of the automotive product. Every experiment isa potential feature, every model is a deployment-ready asset, every data scientist is a product owner of their AI.
The client stopped thinking in terms of"will our AI work in production" and started thinking "which of our working AIs will we scale first". This is the difference between a company that has plans for AI and a company that delivers AI on a daily basis.
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