An AI-powered tool that analyzes workflows, identifies inefficiencies, and suggests automation opportunities using Large Language Models.
This project helps teams understand and improve their internal processes by leveraging AI. Users can input a workflow description, and the system returns a structured analysis including problems, automation opportunities, and AI-driven solutions.
- Analyze workflows in real time
- Identify inefficiencies and bottlenecks
- Suggest AI-based automation strategies
- Generate prototype ideas for implementation
- Export analysis as a Markdown (.md) file
- Simple and interactive UI using Streamlit
Users can:
- Enter a workflow description
- Receive structured AI analysis
- Download the results as a Markdown report
- Our engineering team manually reviews logs and writes daily summaries. This process is slow and inconsistent.
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Problems
- Manual and repetitive work
- Time-consuming process
- Inconsistent results
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Automation Opportunities
- Automate log summarization
- Standardize reporting
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AI Solution
- Use an LLM to extract key insights and generate summaries
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Prototype Idea
- Python script that processes logs and generates summaries using an AI API
- Python
- Streamlit
- Google Gemini API (LLM)
src/
├── main.py
├── analyzer.py
├── prompts.py
git clone https://github.com/YamenRM/workflow-analyzer.git
cd workflow-analyzer
pip install -r requirements.txtSet your API key as an environment variable:
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Windows (PowerShell)
setx GEMINI_API_KEY "your_api_key_here" -
Linux / macOS
export GEMINI_API_KEY="your_api_key_here"
streamlit run main.py
- Engineering workflow optimization
- AI adoption analysis
- Process automation planning
- Internal tooling prototyping
- Structured JSON output
- Workflow visualization (diagrams)
- History tracking of analyses
- Multi-workflow comparison
- Integration with team tools (e.g., GitLab, Notion)
##Author
YamenRM