Executive Summary
Our team validated, corrected, and authored gold-standard automation code to improve Bardeen.ai's natural language interface. The work increased engineering confidence in intent capture and syntax correctness.

Client Background
Bardeen.ai provides workflow automation across SaaS tools. Users describe tasks in plain language, and the system generates playbooks in Bardeen Command Language (BCL) that interact with email, spreadsheets, CRMs, and other services.

Challenge
Automated checks struggled with ambiguous prompts, integration choices, and evolving commands. The team needed experts to validate whether model-generated BCL fulfilled user intent, identify statement-level errors, standardize ambiguous decisions, and author correct code when generations failed.

Solution
A structured annotation framework was implemented for BCL:

• Decision rubric for evaluating targets with consistent rationales

• Guided handling for ambiguous prompts and integration selection

• Specialized review paths for AI actions, enrichment, web search, and triggers

• Gold-standard authoring when model outputs fell short

• Continuous feedback loops with ML and product teams

Implementation

• Secure review flow covering syntax, type safety, plugin availability, and intent fidelity

• Required explanations for incorrect classifications to support error analytics

• Policy handling for prohibited content and unsupported integrations

• Deliverables formatted for immediate pipeline ingestion

Results
Improved precision on difficult prompts, reduced evaluation friction, and accelerated iteration on the natural language interface. Engineering teams gained confidence deploying new capabilities.