HiveAI by Fellowmind

Description (HiveAI)

• Platform and operating model for building and managing a digital workforce of AI agents embedded in business processes
• Built on Microsoft Azure and the Azure AI ecosystem, enabling secure, scalable, and enterprise-grade AI applications
• Leverages Azure AI Foundry for designing, building, and orchestrating AI agents and AI-powered applications, accelerating development from concept to production
• Utilises core Microsoft Azure services such as Azure Kubernetes Service (AKS), Cosmos DB, and Application Insights to provide a robust, cloud-native foundation for agent execution, data management, and observability
• Enables organisations to design, deploy, and operate AI-powered business applications (AI apps) and autonomous agents on a unified platform
• Bridges the gap between AI experimentation and production by utilising Microsoft Azure capabilities for scalability, resilience, and governance
• Combines technology (HiveAI engine on Microsoft Azure), lifecycle framework, and agent portfolio into one coherent delivery model
• Agents autonomously execute tasks, orchestrate workflows, and integrate with Microsoft 365, Dynamics 365, and enterprise data platforms
• Supports development of modular AI apps and agent-based solutions, aligned with modern cloud, microservices, and API-driven architectures
• Built with a human-centric and governed approach, where AI agents are onboarded, monitored, and evolved like digital employees
• Drives productivity, scalability, and measurable business impact, while freeing people for higher-value work

Key Deliverables (HiveAI)

1. Platform & Technology

• HiveAI Engine (Azure-based foundation for agent development and orchestration)
• Secure runtime environment with governance, monitoring, and control capabilities
• Integration into Microsoft 365, Dynamics 365, and other enterprise systems

2. Agent Portfolio

• Library of pre-built AI agents across business functions (e.g. sales, finance, operations)
• Use-case driven accelerators to kickstart value quickly

3. Agent Lifecycle Framework

• Structured lifecycle from idea → design → build → operate → retire
• Governance, KPI definition, risk management, and continuous improvement embedded

4. Delivery & Services

• End-to-end support: strategy, design, implementation, and scaling
• Managed services for monitoring, optimisation, and ongoing evolution of agents

5. Governance & Operating Model

• Standardised approach for security, compliance, and control of AI agents
• Organisational model for managing a hybrid workforce (people + AI)