A stateful AI agent framework powered by the Cognitive Lattice to solve complex tasks with persistent memory and reliable tool orchestration.
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Updated
Oct 7, 2025 - Python
A stateful AI agent framework powered by the Cognitive Lattice to solve complex tasks with persistent memory and reliable tool orchestration.
Project Agora: MVP of the Concordia framework. An ethical, symbiotic AI designed to foster and protect human flourishing.
🤖 NoCapGenAI is a Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, Ollama, MongoDB, and ChromaDB. It features a clean, modern UI and persistent vector memory for context-aware conversations. Easily integrates with Ollama-supported models like phi3:mini, llama3, mistral, and more. Designed to support customizable assistant modes
Secrin is a real-time assistant that gives developers instant context from their code, issues, and docs.
An intelligent, context-aware Q&A backend powered by Groq LLM and Django REST Framework. Supports real-time chat, multi-turn memory, and blazing-fast responses. Seamlessly integrates with a React frontend available in a separate repo.
The Customer Support Ticket Classification and Response System combines advance AI models with RAG to automate and elevate ticket categorisation and response generation. By leveraging multi-model integration, sentiment analysis, urgency detection, and vector-based retrieval, it delivers precise, context-aware responses and actionable insights.
A privacy-first browser extension that detects text inputs on any webpage, and generates context-aware replies using selectable LLMs.
A lightweight Retrieval-Augmented Generation (RAG) agent powered by Groq AI and local embeddings, built to process and understand text data efficiently. It retrieves relevant context from your own files and generates accurate, natural-language responses -all while keeping your data private and running locally.
Artificial-intuition–driven pattern recognition system under high noise & scarce data environments. Validated on real-world datasets with proven generalization, robustness & scalability.
Context-aware tool for automated BDD test generation and execution using RAG, VectorDB, and LLaMA.
🔍 Enhance pattern recognition in noisy environments with limited data, improving predictive maintenance and adaptability across multiple sectors.
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