🚀 BenchCraft AI — AI-Powered API Edge-Case Testing
BenchCraft AI is a Generative AI-powered application designed to automate API edge-case testing and response analysis.
The application takes an API specification and uses Google Gemini to generate synthetic test payloads covering different edge cases. It then sends the test requests to the authorized API and analyzes the responses.
Key Features
AI-generated API test cases
Boundary and validation testing
Missing, null, and incorrect data-type scenarios
Special-character and input-validation testing
Automated HTTP request execution
Response status and response-time analysis
Structured test reports
CSV result export
Tech Stack
Python · Streamlit · Google Gemini API · REST APIs · Requests · Pandas
Key Concepts
Generative AI · API Testing · Test Automation · Edge-Case Testing · Response Analysis · Synthetic Test Generation
Workflow
API Specification → AI Test Generation → Automated API Testing → Response Analysis → Reporting
This project helped me explore how Generative AI can support automated API testing and quality assessment by generating diverse test scenarios and analyzing API behavior.
AI Property Maintenance Automation
AI-powered property maintenance automation designed to streamline how property management teams handle tenant maintenance requests.
The system takes a maintenance request, analyzes the issue, determines its priority and category, recommends a suitable vendor, and automatically creates a structured work order.
Workflow:
Tenant request → AI analysis → Priority & category → Vendor matching → Work order
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted request classification.
This project was built as a portfolio demonstration of AI automation for property management operations.
I help businesses scale through custom AI engineering services.
This video outlines my services for creating consistent AI characters and cinematic AI ads for brands looking to stand out.
Whether you need a full brand film or specific automation tools, I focus on delivering high-quality assets that align with your business goals.
Every project starts with a clear brief and a strategic approach. If you are unsure where to begin, I suggest starting with an AI audit to identify the best opportunities for automation within your current operations.
Let me know in the comments what kind of content you are trying to produce for your brand.
Pydantic catches shape errors, but a plausible wrong insight can still pass. I'd keep a small set of posts with expected labels in LangSmith and rerun it after prompt changes. Are you tracking that kind of drift?