AI Copilot for Custom DSL — Augmenting a 30-Engineer Team
80% time saved
Summary
We built a multi-agent AI system that auto-generates code templates in a custom domain-specific language (DSL) for a global data intelligence company. The system produces high-quality drafts with detailed diagnostic reports, turning a 30-person team's manual template-writing workflow into an AI-assisted review process — saving ~80% of time per template.
Domain: Data Intelligence · E-Commerce Analytics Process type: AI-augmented developer productivity; multi-agent code generation with human-in-the-loop review
The Client Situation
A global data intelligence company processes e-commerce receipt data from millions of users to provide market insights to enterprise clients. To extract structured data (items, prices, order totals) from merchant emails, dedicated teams write parsing programs in a custom Turing-complete DSL — think regular expressions on steroids but with full programming capabilities.
Each merchant has multiple email templates (order confirmation, shipping, cancellation, etc.), and each template requires a custom program. The problem: merchants frequently change their email formats, breaking existing templates. A team of 30 engineers writes and maintains these programs full-time. The work is repetitive, time-consuming, and the backlog of broken or new templates never shrinks.
What We Delivered
Multi-agent code generation system We designed and built a multi-agent AI system that takes merchant email samples as input and automatically generates DSL templates that parse the emails and extract structured data. The system understands the custom language, the email structure, and the extraction requirements — producing complete, working drafts.
Diagnostic reports for fast human review The system doesn't just generate code — it produces a detailed report highlighting problematic sections where the AI is less confident or where edge cases may need attention. Engineers go straight to the flagged areas instead of reviewing everything line by line.
IDE integration The system is designed as a Copilot-style assistant integrated into the engineers' existing IDE. Templates appear as suggestions that engineers can accept, modify, or regenerate — fitting into their existing workflow rather than replacing it.
Human-in-the-loop quality control Generated templates typically require minor corrections. Engineers review and polish the AI output rather than writing from scratch — shifting their role from authors to reviewers.
Outcome
We delivered an AI Copilot for a custom programming language that augments a 30-engineer team's productivity by ~80%. What used to be hours of manual template writing is now a review-and-fix workflow on AI-generated drafts. The MVP is implemented and well-received; IDE integration is underway. The same pattern — AI generates domain-specific code, humans review and refine — applies to any organization with custom languages, DSLs, or repetitive code-generation workflows.