AI Research Platform

Developing and prototyping advanced agent-driven, human-in-the-loop workflows to collaboratively leverage collective intelligence.

Launch Live Demo

Key Impact

Stealth Prototype; exploring innovative frameworks for accelerated and enhanced research capabilities.

Technologies Used

MCPRAGVector DBReactTailwindOllamaVLLMBolt.diy

Project Brief

A stealth prototype aimed at pioneering networked human-agent collaborative workflows to deepen and accelerate complex research processes. Currently exploring and integrating emerging AI technologies and UX methodologies.

Challenges

•Existing research tools, such as OpenAI's "Deep Research," are constrained by limited capabilities in organization, contextual connectivity, and interactive usability within research processes.

•Navigating the rapidly evolving landscape of emerging AI libraries and experimental frameworks, assessing their real-world practicality, and systematically integrating promising technologies while maintaining strategic objectivity.

•Conducting extensive UX research and discovery to ensure intuitive and productive human-agent interactions.

Solution

1. Knowledge work meets GitHub, Reddit, LinkedIn, Obsidian, N8N, and native AI interactivity.

2. Combining RAG, MCP servers, with multi-component prompting to effectively structure and enhance research processes.

3. Incorporating high-performance vector databases, cloud or locally hosted AI models and data, and options for third party data integrations.

4. Role based permissions and "responsibilities" for autonomous agents.

5. An intuitive and context optimized front-end interface with variable context loading for the multiple AI agents the user will interreact with.

Future Development

Ongoing stealth development—contact for more details, collaboration, and funding details. View the early conceptual frontend build by visiting the demo at the top.

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