CONTROLLED AI SYSTEMS
Attnora connects AI agents, company knowledge, models, providers, and tools while keeping permissions, privacy, quality, and costs visible.
Discuss your AI systemBuilt in Switzerland for organisations that need privacy, continuity, and operational control.
One controlled operating layer
Attnora is a Swiss AI systems company that designs and implements controlled AI infrastructure around real workflows. It connects company knowledge, agents, models, providers, and tools through explicit controls for permissions, source provenance, evaluation, deployment, quality, and cost.
Start with the pressure point that matters now. Each offer can stand alone, while sharing the same control layer for permissions, evaluation, provider choice, and cost.
Connect agents to authoritative wikis, documents, repositories, project decisions, and institutional knowledge with citations, freshness, and permission-aware retrieval.
Deploy personal, role-specific, research, coding, support, and operational agents with explicit tools, skills, channels, review steps, and auditability.
Control provider access, model routing, caching, budgets, fallbacks, cost attribution, and runaway loops without slowing useful AI adoption.
Assess and deploy local, open-weight, managed, or hybrid models where privacy, latency, economics, or provider independence justify them.
As Attnora's flagship context layer, Company Memory separates authoritative sources, retrieval, working memory, and agent execution so knowledge stays current, traceable, and governable.
The model provides intelligence. Company Memory provides relevance, provenance, and continuity.
Attnora works from a bounded use case outward. The first system proves context quality, permissions, operating cost, and business value before scope expands.
01
Identify authoritative sources, critical decisions, user roles, current tools, and the workflow where missing context causes measurable friction.
02
Build the retrieval and memory layer around existing systems, then define permissions, citations, freshness rules, and model routing.
03
Give a small user group a controlled agent workspace and evaluate real tasks for quality, continuity, cost, and risk.
04
Harden the successful workflow, monitor behaviour and spend, improve weak context, and expand only where evidence supports it.
Control does not require every workload to run on the same model or in the same environment. It requires explicit decisions at every boundary.
Company Memory retrieves from canonical systems. It does not silently replace them with stale agent memory.
Agents inherit role-based boundaries. A helpful answer must never bypass the permissions applied to the underlying knowledge.
Use frontier, balanced, open, or local models according to complexity, sensitivity, latency, and measured quality.
Track spend per task, team, product, or customer. Use caching, budgets, and circuit breakers to reduce avoidable processing and limit retry or loop exposure.
Citations, evaluation criteria, logs, and human review make consequential agent work inspectable and improvable.
Attnora is suited for teams that handle sensitive information, complex procedures, regulated processes, or high-value institutional knowledge.
Controlled intelligence for research, compliance, operations, and advisory workflows.
Confidential knowledge systems for precedent, research, drafting, and internal expertise.
Governed AI support for sensitive knowledge, documentation, and operational processes.
Private systems for technical knowledge, procedures, maintenance, and internal support.
Controlled AI deployment for complex knowledge environments and public accountability.
Internal AI systems built around proprietary knowledge and confidential workflows.
Reliable AI depends on more than model quality. Attnora makes source authority, retrieval, permissions, tool access, evaluation, deployment, provider routing, and cost visible as one operating system.
Begin with a Company Context Map, Controlled Agent Pilot, AI Spend Audit, or Private AI Feasibility Study. Attnora defines the smallest credible system, builds it, and measures whether it should expand.