AirOps is an AI-driven platform designed to help businesses build and launch AI-powered workflows. Users can choose from text, image, and transcription models, incorporate data retrieval, human review, and over 30 other steps. The platform aims to drive organic growth, optimize workflows, and enhance operational efficiency by leveraging the power of AI and data integration. It is scalable and can be configured in minutes to suit specific business needs.

The Context-First AGI Cognitive Context Engine (CCE) Prototype provides a robust toolkit for developers to implement context-aware AI agents. It leverages vector embeddings to store historical user interactions, enabling efficient retrieval of relevant context snippets. The engine automatically summarizes lengthy conversations to fit within LLM token limits, ensuring continuity and coherence in multi-turn dialogues. Developers can configure context prioritization strategies, manage memory lifecycles, and integrate custom retrieval pipelines. CCE supports modular plugin architectures for embedding providers and storage backends, offering flexibility for scaling across projects. With built-in APIs for storing, querying, and summarizing context, CCE streamlines the creation of personalized conversational applications, virtual assistants, and cognitive agents that require long-term memory retention.

Qdrant is an advanced vector search engine that enables developers to build and deploy AI applications with high efficiency. It excels in managing complex data types and offers capabilities for similarity searches on high-dimensional data. Ideal for applications in recommendation engines, image and video searches, and natural language processing tasks, Qdrant allows users to index and query embeddings quickly. With its scalable architecture and support for various integration methods, Qdrant streamlines the workflow for AI solutions, ensuring rapid response times even under heavy loads.

Khoj AI Agent is an intelligent platform designed for seamless data exploration and knowledge retrieval. By utilizing natural language processing, users can engage in conversational queries, asking questions about datasets or seeking specific information. The agent dynamically interprets the queries, fetches relevant data, and presents insights in an easily understandable manner, making data interactions more accessible and effective.