Co-Sight is an open-source AI framework that simplifies development and deployment of real-time video analytics solutions. It provides modules for video data ingestion, preprocessing, model training, and distributed inference on edge and cloud. With built-in support for object detection, classification, tracking, and pipeline orchestration, Co-Sight ensures low-latency processing and high throughput. Its modular design integrates with popular deep learning libraries and scales seamlessly using Kubernetes. Developers can define pipelines via YAML, deploy with Docker, and monitor performance through a web dashboard. Co-Sight empowers users to build advanced vision applications for smart city surveillance, intelligent transportation, and industrial quality inspection, reducing development time and operational complexity.

Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.

UltiHash is a high-performance, S3-compatible object storage platform designed specifically for AI workloads such as generative AI, model training, retrieval-augmented generation, and data lakehouse environments. It helps teams manage large-scale, unstructured and structured data with optimized throughput, binary-level deduplication, and Kubernetes-native deployment options to improve GPU/TPU efficiency and reduce storage costs.

Sys-Agent provides a secure, self-hosted environment where users issue natural language instructions to perform system-level tasks. It connects with AI backends like OpenAI, local LLMs or other model services, translating prompts into shell commands, file operations, and infrastructure checks. Users can customize prompts, define task templates, scale through Docker or Kubernetes, and extend functionality via plugins. Sys-Agent logs all actions and offers audit trails to ensure transparency and security.