The challenge
Operational workflows depended on repetitive manual steps and slow data movement between interfaces and internal systems.
AI Automation / Backend
GWEN AI Workflow Automation Platform
AI-powered enterprise workflow automation built with Python and FastAPI microservices, orchestration and data pipelines.
Role
Data Scientist / AI Engineer
Context
GWEN AI / Aviato Consulting
Period
2022–2023
What I built
Built Python and FastAPI microservices to automate enterprise workflows, with orchestration around data-processing pipelines and reusable service integrations. Airflow was used for scheduled pipeline execution where appropriate.
Architecture
- FastAPI microservices
- Python workflow orchestration
- Airflow DAGs and ETL pipelines
- API integrations between enterprise systems
- Operational monitoring and error handling
Outcomes
- Approximately 40% reduction in data-processing time
- Approximately 30% reduction in interface task time
- More repeatable and maintainable enterprise workflows
Technology
PythonFastAPIAirflowREST APIsETLSQL