Moiz Ahmed Mansoori
AI Assistants, RAG Chatbots & Business Workflow Automation
New to Contra
Moiz Ahmed is ready for their next project!
I built an AI wellness app that does more than generate responses.
It listens, remembers context, and responds with emotional guardrails in place. https://asksukoon.com/
Most “AI wellness” tools today are just chat interfaces with a mental health label attached.
AskSukoon was built differently.
What’s inside:
— Voice conversations with sentiment-aware responses
— Memory-powered support using ChromaDB
— Guardrails for sensitive interactions
— Guided reflections and mood tracking
— Fast, mobile-friendly experience built for real users
The hardest part wasn’t connecting an LLM.
It was designing something that feels calm, safe, and usable in production.
Shipping this taught me more about product than 3 years of coursework
Live: https://asksukoon.com/
Services: contra.com/moiz_roshan_zszcum4s
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QueryMind lets you query a real database using plain English.
Type a question, get an answer — no SQL knowledge needed.
Built a production-grade AI agent using LangGraph that converts
natural language to SQL, executes it on a real PostgreSQL database
with 100,000+ rows, and automatically corrects errors in a retry
loop if the query fails.
Key features:
7-node LangGraph agent with conditional routing and self-correction
pgvector semantic search for intelligent schema retrieval
Full observability dashboard with real-time metrics and query history
Trace replay — see every step the agent took to answer your question
Deployed live: Next.js frontend on Vercel, FastAPI backend on Render
Tech: LangGraph · Groq LLaMA 3.3 70B · PostgreSQL · pgvector ·
FastAPI · Next.js · Tailwind CSS
Github: https://github.com/moiz-mansoori/QueryMind-NL2SQL-Agent-System
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Built AIRO, a 6-agent ML automation system using LangGraph and Groq LLaMA 3.3. The system automates the entire ML pipeline from data ingestion to final report generation. Ran 18 experiments achieving RandomForest f1_macro of 0.7329. Features parallel training, MLflow tracking, SHAP explainability, and a dark Streamlit UI with live log streaming.
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Built a deployed AI tool that lets users upload research papers and query them using natural language. RAG pipeline with vector search retrieves precise context-aware answers. Features PDF parsing, AI summaries, topic modeling, and citation extraction. Live on Render.
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Built a full-stack Todo app across 5 phases — CLI to cloud-native. Phase 3 adds AI chatbot via LangChain and Groq for natural language task management. Phase 4 containerized with Docker and Kubernetes. Phase 5 deployed on Vercel with Neon PostgreSQL. 4 live deployments.
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Built a deep learning system to detect brain tumors from MRI scans using VGG16 transfer learning. Integrated Grad-CAM heatmaps for explainable AI output. Achieved 94.9% confidence with 4-class classification. Full pipeline from raw MRI input to visual diagnosis output.
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Built an empathetic mental wellness chatbot using LLaMA 3.3 and LangChain. Features mood-based conversations, real-time sentiment analysis, ChromaDB vector memory, breathing exercises, and journaling prompts. Deployed live on Streamlit Cloud.
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