I do not believe LLMs are useless. They are extraordinary reasoning and language engines, but they are not complete enterprise systems. They can lose context, backtrack in long conversations, hallucinate and produce different answers to the same question. They also do not inherently understand an organisation’s permissions, relationships, institutional history or risk boundaries. Treating the model itself as the product therefore creates a fragile architecture.

Twin1 separates the model layer from the governed context and coordination layer. We connect to the systems where work happens, retrieve only the relevant permission-filtered material, maintain memory and interaction history, and assemble the context required for a particular task. The model then reasons over that bounded context, with source citations, enterprise policies, inherited permissions and human approval applied around it. The complete customer corpus is not sent to a model for every question.

We are also model-agnostic. We can route work across proprietary or open-source models and support SaaS, single-tenant or private-cloud deployments. That matters for resilience, economics and AI sovereignty, but it also reflects a deeper architectural belief: the language model is a component, not the product or the moat.

Our durable value lies in the context the public models cannot possess: an individual’s knowledge, judgment, relationships and working history, together with the governance framework that determines how that knowledge can be used. The Twin Network then allows people and agents to find the right expertise and coordinate work without removing control from the humans they represent.

So we are not betting against foundational models. We are building the memory, retrieval, governance, orchestration and human-control layers required to turn increasingly capable but imperfect models into a dependable enterprise system.