Building
AI-Native
Test Automation

Problem: Manual test case writing is slow, inconsistent, and error-prone.

Solution: Upload PDF requirements → AI extracts and generates structured test cases (Positive, Negative, Edge cases).

Key Features:

• PDF document parsing with PyMuPDF
• Dual LLM support (Ollama free / OpenAI paid)
• Export to Excel, JSON, Markdown
• Token tracking & cost awareness

Tech Stack: Python, LangChain, Ollama, OpenAI, Pydantic, Streamlit

Problem: Poor resume quality leads to no interview calls despite having skills.

Solution: AI analyzes resume against job descriptions, provides scoring and actionable improvements.

Key Features:

• Resume parsing & skill extraction
• Job description matching (0-100 score)
• ATS compatibility scoring
• Section-wise improvement suggestions

Tech Stack: Python, RAG, LLM, PDF Parser, Streamlit

Problem: Students struggle to study large PDFs and find relevant information quickly.

Solution: Upload documents → Ask questions → Get accurate answers with source citations.

Key Features:

• Multi-document upload support
• Semantic chunking & embeddings
• Vector similarity search
• Source citation with page numbers

Tech Stack: RAG, ChromaDB, Embeddings, LangChain, Streamlit

Problem: Manual customer review analysis is slow, biased, and doesn't scale.

Solution: Upload reviews in bulk → AI analyzes sentiment, themes, and generates reports.

Key Features:

• Batch CSV/Excel processing
• Sentiment classification
• Theme extraction & categorization
• Export reports (PDF, Excel)

Tech Stack: Python, LLM, Sentiment Analysis, Batch Processing

Problem: Important meeting points and action items get lost or forgotten.

Solution: Upload meeting transcript/recording → AI extracts summary, decisions, and action items.

Key Features:

• Audio/text transcript processing
• Key points extraction
• Action items with owners
• Shareable summary reports

Tech Stack: Python, Transcription, LLM Summarization, Streamlit