TEQnation 2024: Automind: Enterprise-Grade AI Assistants Done Right – Erik Meijer

Introducing Automind: Enterprise-Grade AI Assistants Done Right While creating autonomous AI agents may seem straightforward, deploying them in enterprise environments raises significant challenges around security, reliability, scalability, usability, and integration. Simply combining off-the-shelf ingredients like language models and retrieval tools is akin to a home cook attempting a Michelin-starred meal – disastrous results are likely. We recognized that building truly robust, enterprise-grade AI assistants requires deep expertise spanning AI, systems infrastructure, security, and software engineering. Our Automind platform is founded on sophisticated technical insights that integrate classic computer science with modern large language models. Informed by decades of experience at companies like Microsoft and Meta, the Automind platform prioritizes enterprise requirements like security, reliability, and scalability. Our AI agents run as concurrent, fault-tolerant actors with persistent memory and hot-swappable models. Dynamic guardrails and access controls ensure data privacy and safe model behavior. State-of-the-art AI components like intelligent routing, semantic indexing, and local re-ranking produce accurate, trustworthy results. Infinite memory lets users refer back to past conversations. Careful lineage tracking enables citation, auditing, and compliance. A no-code workflow instruction paradigm allows anyone to train custom assistants through simple annotations, without programming. These assistants can then generalize to solve new problems. Leveraging techniques from compilers, Automind can even automatically fine-tune and deploy custom models per enterprise, team, or individual. In this talk, we introduce Automind and share the key insights that transformed a seemingly simple task into a formidable technical challenge – and how we overcame it. “It is unbecoming for accomplished individuals to expend valuable time on tasks that could be efficiently handled by machines or delegated to others.” Gottfried Wilhelm Leibniz (1646-1716)