BlaBlaCar builds a Data Copilot to shift data analysis left with LLMs and GitHub Copilot
Software Engineers at BlaBlaCar were blocked from self-service data analysis by unfamiliar tooling — SQL dialects, DBT, and Airflow — while Data Analysts were overwhelmed with ad-hoc 'quick questions', producing unmaintainable spaghetti SQL scattered across notebooks and sheets, and losing institutional knowledge in ephemeral Slack messages.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineer asks question in IDE
An engineer asks a data question directly inside VS Code.
Questions that used to sit in a Jira backlog for 3 weeks are now answered in 10 minutes inside VS Code; engineers validate feature ideas with data autonomously, and Data Analysts are freed from the support queue to focus on deep-dive modeling.
What failed first
Generic AI assistants and existing Text-to-SQL tools were found unsuitable: generic AI tools lack business-specific context, and text-to-SQL products are designed for non-technical business users rather than engineers.