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Development of Open-Source Collaborative Conversational AI Platform with Real-Time Multi-User Support and LLM Integration
  1. case
  2. Development of Open-Source Collaborative Conversational AI Platform with Real-Time Multi-User Support and LLM Integration

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Development of Open-Source Collaborative Conversational AI Platform with Real-Time Multi-User Support and LLM Integration

vstorm.co
Information technology
Environmental Services

Challenges in Secure, Collaborative LLM Utilization

Organizations face difficulties in securely deploying and managing LLMs for collaborative workflows due to fragmented tools, lack of real-time multi-user support, insufficient prompt engineering capabilities, and risks associated with data privacy, transparency, and control over AI models trained on proprietary data.

About the Client

A California-based startup focused on enhancing online interactions through open-source annotation tools and AI-driven collaboration platforms, leveraging expertise in climate change and large language models (LLMs).

Key Goals for AI Collaboration Platform

  • Create an open-source, user-friendly platform for real-time collaborative use of multiple LLMs
  • Enable secure self-hosting options to ensure data security and control
  • Develop a dual-layer system for organizational communication and advanced prompt engineering
  • Integrate reusable prompt libraries and interoperability with diverse LLMs
  • Implement memory functions for contextual continuity in conversations
  • Ensure seamless integration with external ecosystems like GSuite

Core Platform Capabilities

  • Dual chat+layer interface for communication and prompt design
  • Multi-LLM support (API-based and self-hosted models)
  • Reusable prompt library with version control
  • Real-time collaboration with role-based permissions
  • Memory retention for contextual conversation history
  • LangChain-powered automation for content workflows
  • GSuite and external application integrations

Technology Stack Requirements

LangChain framework
Open-source LLM frameworks (e.g., Hugging Face, Llama)
Real-time collaboration protocols (e.g., WebSockets)
Containerization (Docker/Kubernetes)
Cloud-agnostic architecture

System Integration Needs

  • Google Workspace (GSuite) APIs
  • Third-party LLM providers (OpenAI, Anthropic)
  • Authentication systems (OAuth2, SSO)
  • Cloud storage solutions

Operational Requirements

  • Enterprise-grade data encryption
  • Horizontal scalability for concurrent users
  • Low-latency real-time collaboration
  • Compliance with data sovereignty regulations
  • High availability and fault tolerance

Expected Business Outcomes

Enables organizations to securely leverage LLMs for collaborative workflows, improving productivity through real-time AI-assisted content creation, reducing operational risks via self-hosted options, and fostering innovation through reusable prompt engineering capabilities. The platform will enhance cross-domain collaboration with transparent AI interactions while maintaining strict data governance.

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