Simmis grew from a decade of building multi-model, versioned, and versatile data systems, and from decades of research into probabilistic reasoning, distributed systems, artificial intelligence, and organizational learning. This deep foundation makes Simmis well positioned to tackle the problem of AI adoption for organizations, because we understand that an organization is shaped as much by how it coordinates as by the intelligence of its members. Intelligent people or agents do not automatically produce coherent progress.
If knowledge, authority, decisions, and work remain divided across applications, documents, and individual memory without proper governance, then organizational AI adoptions will fail. Because before AI, people could bridge those gaps with meetings, handoffs, and experience. But with AI, activities and information flow faster than this human coordination can assemble context, reconcile change, and preserve what the organization learns.
No organization can adopt AI if they cannot govern it, the workflows around it, and the data used by it.
Simmis echoes simulation. Versioned state preserves the past, while forkable state preserves alternatives. The longer research horizon is an organization that can compare possible futures, act, and learn when reality differs.
That simulation layer remains research. Its foundation is already part of the Simmis architecture. Read the longer research horizon.
ARCHITECTURE · ENGINEERING · AI/ML
Christian built the underlying stack from the database upward, applied it in his previous ventures, and completed a PhD. He leads the architecture that connects durable state, execution, and agent participation.
If fragmented knowledge, disconnected tools, uncoordinated work, or difficult AI adoption is creating problems in your organization, we want to hear about it.
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