You don't need a monolithic, all-knowing model. You need the right expert, for the right task, verified and yours.
The frontier labs built token predictors at planetary scale. Extraordinary engineering — wrong architecture for your problem. You don't need access to the sum of human knowledge. You need a model that knows your domain, verifies its own work, and belongs to you.
Built in Australia. Deployed on sovereign infrastructure. Ready now.
The architecture is the product. The models are replaceable components.
Annie is not a model. It is a verified orchestration platform — a system of twelve pipeline roles designed to classify, route, judge, and verify expert responses. Evari ships Annie with our own sovereign foundation models as proof of concept. But every slot is open. A bank brings their own risk model. A defence contractor plugs in a classified domain specialist. A health service adds a clinical decision model. Each runs through the same classification, consensus, and verification pipeline. The platform is the moat. The models are yours.
Twelve open pipeline roles. Bring your own models, your own domain knowledge, your own competitive advantage. Annie orchestrates them.
Every model's output is cross-checked by independent peers using domain rubrics, then verified against the original prompt. Hallucination is a managed risk.
Each new model on the platform is additional compute demand on the infrastructure hosting it. Your value grows as the ecosystem grows.
Access to our Workforce Foundation Models means you focus on domain context. We handle the rest.
Building a custom model from scratch is expensive and slow. Our Workforce Foundation Model suite — a collection of sovereign, pre-trained models optimised for the hierarchical mixture of experts architecture — cuts that cycle time dramatically. You bring the domain knowledge, the business context, the data that makes your model yours. We provide the foundation, the orchestration, and the verification pipeline. The result: custom AI that is accurate, auditable, and deployed in weeks, not months.
Domain-specific fine-tuning on your data, not training from scratch. Weeks to deployment, not months.
Fine-tuning runs cost less than $5 per iteration — continuous improvement without the million-dollar retraining bill.
The MoE architecture means your custom model benefits from the verification pipeline and peer consensus, not just raw parameter count.
Annie is the only enterprise AI platform where you plug in your own models and keep your competitive advantage.
Twelve open pipeline roles. Evari provides the foundation models as proof of concept, but every slot is replaceable. A bank's proprietary risk model runs alongside Evari's general models. A defence contractor's classified specialist never leaves their infrastructure.
Multi-model consensus and verification is built into the platform, not bolted on as a feature.
Every expert response is scored by a Judgment Panel using domain rubrics, verified against the original prompt, and re-routed if it fails. Hallucination reduction of 4–67% vs. single-model approaches. Calibration error reduced by 49–74% across medical benchmarks.
Your models improve on your data automatically, in off-peak cycles, for less than $5 per run.
Every interaction flows through the Cognition Stream. Sleep cycles fine-tune specialists on real user data. No manual retraining. No million-dollar bills. No waiting for a provider to release a new version.
10–100× lower inference cost than frontier APIs at enterprise volume — because the architecture is built for efficiency, not scale-at-any-cost.
Specialists run on standard inference GPUs. Intelligent routing directs 80–95% of queries to the most cost-efficient path. Multi-model consensus costs less than single-model inference at frontier scale.
| Volume | Annie | Frontier API |
|---|---|---|
| 100M tokens/day (annual) | $18K – $73K | $550K – $1.8M |
Every component deploys on infrastructure you control. No external API calls. No data leaves your jurisdiction. No single point of failure.
Base model trained from scratch — no dependency on external weights. Fully air-gappable. Every model, every inference step, every verification decision runs on your hardware. Provider outages, export controls, geopolitical events, and platform decisions made in another country's boardroom — none of these reach you.
Australia's sovereign AI compute is being built. Annie is the workload that fills it.
There is $1.2 billion in sovereign AI-optimised compute under development in Australia. The question every data centre operator is answering is: what runs on it? Annie generates high-value, long-term AI inference workloads in the sectors — government, defence, financial services, healthcare — that need sovereign infrastructure most. And because Annie is a platform, every new customer who brings their own models is additional compute demand on the infrastructure that hosts it.
Each Annie customer brings their own models. More customers means more models means more compute demand on your infrastructure.
Runs on standard inference GPUs at moderate power density — high revenue per rack without training-cluster overhead.
Position as the data centre running Australia's sovereign AI platform — not just infrastructure, but the application layer that makes it valuable.
PROTECTED workload certification, Five Eyes interoperability, zero external dependency.
APRA compliance, domain accuracy, complete audit trail.
Patient data sovereignty, clinical accuracy requirements, explainable reasoning.
Policy and claims processing with a verifiable decision record.
Jurisdictional data control, explainable reasoning chains, regulatory compliance.
Annie is powered by QuivaWorks — the agentic AI builder platform from Evari. QuivaWorks provides the orchestration, routing, and workflow infrastructure that makes Annie's platform architecture possible, and is available independently for organisations building their own AI-native workflows.
Explore QuivaWorks at quiva.ai →AI that's yours. Completely.
Built in Australia. Deployed on sovereign infrastructure. Ready now.