CODES AI (Internal)
Pioneered an AI-assisted development framework using local LLMs to automate data warehouse design, standardize ETL pipelines, and manage Azure cloud infrastructure code.
Traditional data engineering projects require weeks of repetitive boilerplate — schema design, ETL pipeline code, infrastructure templates, and testing. Senior engineers spend 60%+ of their time on code that follows predictable patterns rather than solving novel problems.
Built a comprehensive AI-assisted development framework using local LLMs (Claude, custom models) that automates the repetitive parts of data engineering. The system generates data warehouse schemas from business requirements, creates ETL pipeline code from data mappings, and produces Azure infrastructure templates from architecture diagrams.
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Faster Development
Reduced development time across all data engineering projects
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Code Output
Engineers produce 3× more deliverables per sprint
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First-Pass Quality
AI-generated code passes review with minimal changes
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Security Issues
Automated OWASP audit on every generated module
Engineer describes what they need in plain English — 'Build a star schema for sales data with SCD Type 2 on customer dimension'.
Claude analyses the requirement and generates the full database schema, ETL mapping document, and architecture diagram.
Cursor agent mode generates production PySpark code, SQL DDL, Data Factory pipelines, and Azure DevOps YAML.
AI reviews generated code for security (OWASP), performance (query optimization), and best practices compliance.
One-click deployment to Azure with automated testing, monitoring dashboards, and performance baselines.
Free consultation and project estimate within 24 hours.