Intelligence Independence
Sovereign AI you actually control, from the first training run to a served endpoint.
Fuzzball runs AI across infrastructure you already operate. You choose the models, choose where they run, and choose what they can reach.
CIQ trusted by:
Have you rented away your choices?
Who decides what your AI runs, where it runs, and what happens to your data? For most enterprises, the answer is a vendor. You did not choose that dependency. You accumulated it, one API key and one token bill at a time.
Intelligence Independence gives you that decision back: who runs your AI, and whether it is sovereign at all.
Open models have closed the capability gap for most enterprise work. What they add is control a commercial API was never going to give you.
Choose your models
The right model depends on the job. Independence is getting to choose, and to change your mind.
Run open-weight models like Llama, Qwen, and Mistral on your own hardware. Go fully open with OpenWALDO, where the training data is as open as the weights. Reach a frontier API when a workload calls for it. Or combine them, one model per workload.
- Open-weight models
- Fully open models
- Frontier APIs
- One-parameter swaps
Choose where they run
The same workflow runs on-prem and on every major cloud, on NVIDIA and AMD.
You define what the job needs. Federate places it: sensitive work stays on-prem, GPU-hungry work bursts to the cloud, overflow spills where capacity is. As chip vendors buy up the schedulers, Fuzzball stays independent of any single one.
- On-prem + any cloud
- NVIDIA and AMD
- Slurm & PBS backends
- Portable workflows
Choose how they reach your data
Every prompt sent to a commercial API is data you don’t get back.
On Fuzzball, inference, chat, and your document knowledge base run inside your environment. You decide which models and agents can reach which data, nothing crosses a boundary you did not draw, and every access is logged. Controlling your data is the whole reason to run your own AI.
- Local inference
- Built-in retrieval
- Persistent vector DB
- Full audit trail
Owning your AI shouldn't mean operating it by hand
An AI agent can run the work for you, inside your walls and under your rules. That is where the Model Context Protocol (MCP) and a workflow-scoped API turn sovereign AI from something you host into something you operate.
Agents run the cluster
Workflows launch workflows
Agents stay sovereign
“Fuzzball lets us turn bespoke workflows into stable, reproducible automation we can stand behind.”
FYR Bio runs its precision-medicine pipelines entirely inside its own environment, from raw signal to results. One platform, production scale, and nothing leaves.
“We have built our own training and inference stack because control over the full model lifecycle is fundamental to how Arcee operates. Fuzzball gives us an additional orchestration layer for coordinating workloads across heterogeneous compute environments without replacing the systems our team has built.”
Learn more in the Sovereign AI Technical Brief
Put sovereign AI to work on your stack.
Let our sales engineers show you how straightforward it is to run AI workloads in a sovereign environment, then work through what your own would take.
Frequently asked
Sovereign AI is running the full AI lifecycle on infrastructure your organization owns and controls, with no external AI provider in the path. That covers training, fine-tuning, inference, and the data behind them. Your models, prompts, and data stay inside a boundary you define.
Intelligence Independence is CIQ’s framing for sovereign AI: you choose your models, choose where they run, and choose what they can reach. It moves the goal past keeping data in-region to ending dependency on any single model provider, cloud, or chip vendor.
With a commercial API, the provider sets the model version and price and decides what happens to your prompts. Sovereign AI runs on hardware you control, so you keep that control and your data never leaves your environment.
Yes. Fuzzball runs any open-weight model on your hardware and lets you swap models by changing one field in a workflow. You can also run fully open models like OpenWALDO, or call a frontier API such as OpenAI or Anthropic when a specific workload calls for it, mixing model types per job.
The same Fuzzball workflow runs on-premises and across AWS, Google Cloud, Azure, and Oracle, on both NVIDIA and AMD GPUs. Fuzzball places each job by data locality, cost, and policy, with no rewrite when the location changes.
Yes. Fuzzball ships an MCP (Model Context Protocol) server, so an AI agent can inspect, draft, and run workflows within limits you set. Reads are open, while writes, runs, and deletes wait for your approval. The agent runs inside your environment, so your data never reaches a third-party service.
OpenWALDO is a fully open AI effort, sponsored by CIQ, where the training data is as open as the model weights. Fuzzball can run OpenWALDO models on your own infrastructure alongside open-weight and frontier options.
Still have questions?
