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Clifton C.

@cliftonc20

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AI Developer | Full Stack Developer

$100 USD / Hour

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United States (1:01 PM)

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Joined on March 20, 2026

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1 Recommendation

$100 USD / Hour

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I help businesses automate workflows and save time using AI. I build custom GPT-powered chatbots, document processing tools, and automation systems that reduce manual work and improve efficiency. Whether you need a chatbot for your website, AI-powered data processing, or workflow automation, I can deliver a reliable solution quickly. What I can build for you: • AI chatbots (customer support, FAQ, internal tools) • Document Q&A systems (PDF, CSV, knowledge base) • GPT-powered automation workflows (Zapier / Make) • Custom backend APIs using Python and FastAPI Tech stack: Python • OpenAI (GPT-4/5) • FastAPI • Zapier / Make • REST APIs • PostgreSQL Why work with me: • Fast delivery and clear communication • Focus on business results, not just code • Clean, scalable, and production-ready solutions Let’s build something that saves you time and grows your business.

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Portfolio

Portfolio

13862506
Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

13862505
Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

13862500
Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

13862494
GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

13862506
Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

Proxy-Tool is a cross-platform proxy management platform that combines OS-level traffic interception, a proxy routing engine, and a cloud-based marketplace and management backend. The system was designed as a modular, API-driven architecture to support large-scale proxy traffic management, provider integration, and real-time analytics while maintaining high performance and reliability. The platform consists of three main components: a web platform for proxy marketplace, billing, and analytics; backend services for authentication, licensing, proxy inventory, and traffic metering; and cross-platform desktop clients (Windows and macOS) that integrate directly with the operating system’s networking stack to provide system-wide proxy routing and traffic control. On Windows, the client integrates with the Windows networking stack using low-level packet interception, while on macOS it uses native Network Extensions to capture and redirect traffic securely. Captured traffic is routed through

Proxy-Tool

13862505
Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

Built a macOS-native real-time audio translation plugin designed for live audio environments, integrated directly into Waves SuperRack and Dante-enabled systems. The plugin performs low-latency speech recognition, translation, and speech synthesis in real time, supporting both cloud-based AI (OpenAI Realtime) and offline local models (Whisper.cpp, MarianMT, ONNX Runtime). The system was architected using a streaming pipeline with WebSocket transport, audio chunking, and buffer-based processing to achieve low-latency performance (~3 seconds end-to-end). The plugin dynamically switches between online and offline modes depending on network availability and cost considerations. Designed with a hybrid edge–cloud AI architecture, the system includes telemetry, session control, and performance monitoring to ensure reliability in live production environments such as conferences, broadcast, and multilingual events. Role: Lead AI Engineer & macOS Plugin Developer Focus: Real-time audio pr

AI Real-Time Translator

13862500
Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

Developed an end-to-end AI Document Intelligence Platform that automates document collection, processing, and knowledge extraction. The system uses web scraping to collect PDF documents, OCR to extract text from scanned files, and large language models to extract structured event information such as participants, dates, locations, and event descriptions. Extracted data is stored in PostgreSQL, and document embeddings are stored using pgvector to enable semantic search. The platform implements a Retrieval-Augmented Generation (RAG) architecture that allows users to ask natural language questions and receive answers with source citations from the original documents. The system includes a fully automated processing pipeline, background workers, and an admin dashboard for managing document ingestion and processing jobs. Role: Lead AI Engineer & Full Stack Developer — Designed and built an end-to-end AI document intelligence and RAG platform from system architecture to full implement

DocIntel AI

13862494
GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

GPP platform is a cloud-based energy metering, analytics, and forecasting platform built to collect, process, analyze, and predict high-volume utility consumption data in near real time. The system integrates with smart meters through DENT Cloud, stores large-scale time-series data, and applies machine learning models to forecast future energy usage, detect anomalies, and support data-driven decision-making to reduce operational and energy costs. The platform was designed as a scalable, production-grade SaaS system with secure APIs, a modern frontend, and a clear separation between data ingestion, analytics, AI prediction, and application services. What I Built -Designed the overall system architecture for a scalable SaaS energy analytics and forecasting platform -Developed a Node.js backend with MySQL for reliable ingestion and storage of high-frequency time-series meter data -Built a modern, high-performance frontend using Next.js for dashboards, analytics views, and forecasting vi

GPP Platform

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