Solara AI is an end-to-end voicebot and web chatbot for dental practices (Live Demo: https://solara.supermia.ai/). Built using LiveKit SIP, OpenAI GPT-4o, ElevenLabs, and FastAPI, it handles inbound/outbound phone calls and website chat. It answers patient queries, checks real-time calendar availability, and books appointments on Google Calendar. Featuring noise cancellation and WebSockets, it provides sub-second voice latency and natural conversation flows to automate dental clinic scheduling 24/7.
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I designed and built Pristine's entire digital operations platform, from database architecture and role-based workflows to a production-deployed web suite and an AI analytics layer.
The challenge was replacing a ceramic processing plant's paper-and-spreadsheet production logs with a system that floor operators would actually use, while giving management the traceability and insight the paper trail never could. Every decision focused on data integrity, least-privilege security, and keeping the interface simple enough for shift operators to work quickly.
I built four services around a single PostgreSQL schema. The operator app handles the full production lifecycle: raw material intake, drying plant shifts, multi-section ball mill batch entry, quality control, and finished-goods inventory, with Excel export throughout and navigation that adapts to each of eight distinct plant roles. A modular Express and Prisma API enforces that access control at the route level. A separate React-Admin console keeps master data (mills, grades, recipes, suppliers, users) out of the operator's way.
The most important piece was traceability. I designed a quality control gate that sits between production output and inventory. QC records are created automatically when a batch completes, reviewed by an inspector, and only generate an inventory entry if quality passes. Every finished bag carries an auto-derived lot number encoding its production date, flow rate, monthly sequence, and bag count, so any outbound shipment can be traced back through inventory and QC to the exact mill batch that produced it.
On top of that I built an AI analytics agent that lets managers query the plant in plain English, architected around a zero-data-to-LLM guarantee. The model only ever sees the database schema and the question, never a row of production data. Five independent layers enforce it: two least-privilege Postgres roles separating read-only queries from insert-only auditing, a sqlglot AST guard that rejects any non-SELECT statement, a per-role table allowlist, the prompt design itself, and a full audit log of every interaction. Answers stream to the browser over Server-Sent Events through a LangGraph pipeline that routes, guards, plans the query, executes, then explains the result in business terms.
Tech Stack: React 19, TypeScript, Ant Design, TanStack Query, Tailwind CSS v4, Node.js, Express, Prisma, PostgreSQL, React-Admin, MUI, Python 3.12, FastAPI, LangGraph, OpenAI, AWS EC2, Nginx, Supabase, Vercel.
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Developed an AI-powered Marketing Campaign Automation platform that helps businesses create, manage, and optimize marketing campaigns across multiple channels with minimal manual effort. The solution leverages Generative AI and conversational AI to streamline campaign planning, content generation, audience targeting, and performance analysis. The platform enables marketing teams to launch campaigns faster while improving engagement and conversion rates. Inspired by modern AI-driven marketing approaches that automate campaign workflows and personalized customer interactions.
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🚀 Built Deploy Doctor for the Zerops Challenge.
Deploy Doctor analyzes GitHub repositories, detects the technology stack, identifies deployment risks, and generates a Zerops-ready configuration automatically.
The goal is to help developers reduce deployment errors and move from repository to production with confidence.
Key Features:
• Repository Analysis
• Stack Detection
• Deployment Risk Assessment
• Automated Zerops Configuration Generation
• Deployment Readiness Reports
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Designed and developed a bilingual (English/Arabic) real-time AI Voice and Chat Agent for ILAF Insurance to automate motor accident claim reporting and document intake (POC: https://ilaf-frontend.vercel.app). Built a low-latency WebRTC voice pipeline using LiveKit Agents, OpenAI Realtime API, Silero VAD, and Deepgram. Developed an asynchronous FastAPI backend with automated Civil ID policy verification, garage assignment, multi-document uploads (Police reports, licenses, damage photos), and automated email notifications, paired with a modern React 19 frontend.
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