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Accelerating DSL Code Generation with AI Powered Virtual Assistant

Retail-Tech

A retail-tech platform company relied heavily on a domain-specific language (DSL) to describe complex data transformations and logic for their business rules. While DSL allowed for a high degree of customization, crafting each rule was a specialized task that required significant time and in-depth knowledge.

Challenges

The team needed highly specialized knowledge in DSL, requiring extensive training for anyone handling these complex tasks.

Each month, 200-250 hours were dedicated to manual DSL coding, an intensive process that strained resources and slowed down operations.

Due to the intricate syntax and logic required, errors were frequent, causing high rework costs and delays. These setbacks impacted overall project timelines and occasionally disrupted launch schedules.

Solutions

Introduced a virtual assistant powered by AI, capable of translating English-based business rule descriptions into precise DSL code. This automation allowed the team to work faster and freed them from the need for constant DSL training.
Leveraging a Large Language Model (LLM), the virtual assistant was programmed to understand English instructions and converted them accurately into DSL, reducing the burden of manual coding while maintaining high standards.
DSL syntax validation was embedded within the assistant, significantly reducing the margin for error and ensuring accurate code from the outset.
Built on a Python and LLM foundation, this solution enabled the team to efficiently process tasks while focusing on quality and scalability, setting the stage for a more streamlined workflow.

Outcome

Enhanced Efficiency
With the virtual assistant, the time spent per business rule dropped from 20–30 minutes to just 10 minutes, doubling productivity on each task.
Significant Time Savings
By saving approximately 100 hours each month, the team could shift focus to other high-impact initiatives.
Approximately 60% Cost Reduction
This approach delivered a 50% boost in efficiency per business rule, alongside a 59.96% reduction in costs compared to manual methods.

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