Joint Reference Architecture · Wand AI × vlno

Sovereign AI on open-weight models, with robustness trained into the weights.

Wand AI runs the sovereign agentic stack. vlno hardens the model itself, so an agent built on it cannot be hijacked. Re-certified on every checkpoint. Existing runtime and privacy controls stay in place on top.

Read the announcement Explore the architecture

01 / The Problem

Real sovereignty needs models a nation can both control and defend.

Sovereignty and cost are pushing the market to open weights. Frontier open models now come close to closed-lab quality, and running them on sovereign compute keeps data and control inside the customer’s border.

But the security posture that made closed frontier models acceptable does not transfer. There is no accountable vendor. There are no published safety evaluations. There is no patching path. Standardized adversarial benchmarks for agentic failure modes are largely missing. In practice, the business case for open models gets approved and the CISO blocks the migration on robustness grounds.

The choice becomes a model you control, or a model you can defend. Not both.

02 / The Solution

One sovereign backend. Open models. Robustness trained into the weights.

Two stacks that meet at a clean seam. Wand AI runs the sovereign agentic backend. vlno hardens the model itself.

Wand AI provides the sovereign stack.
Country-level governance, the hybrid human-AI OS, certified AI labor and autonomous agents, attached to the sovereign compute base.
vlno provides the model robustness layer.
An automated, high-scale adversarial pipeline generates attacks against the customer’s own agentic workflows, runs them at scale in sandboxed environments, and returns training data (RL/DPO-ready trajectories, or a hardening LoRA) that drops into the customer’s fine-tuning pipeline. The hardening lives in the weights. Existing filters and runtime controls stay in place on top.
The artifact is data, not weights or workflows.
Nothing sensitive leaves the customer’s trust boundary. Robustness is continuous, re-certified on every checkpoint, version, and newly published attack class.

The joint stack

Wand AI

Sovereign agentic stack

→

Adaptive Router

Wand · picks the model from the Open Model Registry

→

vlno robustness

Hardening in the weights · certification at admission

→

Any open model

On one sovereign backend, hardened

03 / The Impact

Robustness becomes an admission criterion, not an assurance.

What changes in practice for a sovereign program moving to open weights.

Open models cleared for production

A measured attack-success rate replaces vendor assurance as the basis for the go/no-go decision.

Robustness as admission

Models must meet a robustness bar before they enter the sovereign registry. Not a launch feature. A gate.

Continuously current

Every fine-tune, version, and newly published attack class triggers re-measurement. Posture is never a point-in-time report.

Inside the trust boundary

The artifact is data. No weights or sensitive workflow content leave the customer’s environment.

Better Together

Two stacks, one sovereign backend.

Each side owns what it is built to own. The seam between them is where robustness enters.

Wand AI brings

Country-level governance for AI in production
The hybrid human-AI operating system
Certified AI labor and autonomous agents
Attached to the sovereign compute base
Leadership across every agentic layer

vlno brings

The model robustness layer
An adaptive adversarial pipeline that hardens the model in the weights
Continuous re-certification bound to the weights
Measured by attack success rate against an adaptive attacker
One of very few teams specializing in post-training and the weights

Credibility. Wand AI runs production deployments with major public-sector and enterprise institutions. vlno was founded by leaders in frontier-model red-teaming, adversarial evaluation, and RL-based model hardening. [Wand credibility line to be replaced with Wand’s own approved boilerplate before publish.]

Where vlno plugs in

One seam. Every model hardened.

Wand’s Adaptive Router reduces every model call to one dispatch seam and selects from the Open Model Registry. vlno plugs in at two points along that seam.

Seam 1 · Certification at admission

Robustness as an entry criterion for the Open Model Registry.

vlno’s benchmark measures candidate models before they enter the registry, producing a measured attack success rate under adaptive attacks across agentic scenarios. Robustness becomes an admission criterion of the backend, not a retrofit. Models are tested as served through the hosting used, so the measurement reflects the actual serving path a downstream agent will hit.

Seam 2 · Hardening in the weights

For models that fall short, vlno returns the training data.

vlno generates scenarios in the customer’s operating context, executes them with an adaptive attack model against sandboxed agentic surfaces, and returns RL trajectory data or a hardening LoRA. The customer applies the artifact in their own environment. Nothing sensitive leaves their trust boundary.

Open Model Registry · illustrative view

Illustrative only. Model identifiers are generic and do not imply any specific vendor endorsement.

model_id	Runtime	Robustness
oss-model-alpha-70b-instruct	sovereign vLLM	certified via vlno · target ASR met
oss-model-beta-72b-agentic	sovereign vLLM	hardening LoRA available
oss-model-gamma-large	sovereign vLLM	re-certification in progress
oss-model-delta-instruct	sovereign vLLM	hardening LoRA available

The certificate is bound to the weights. It expires when the weights change. Every fine-tune, version, and newly published attack class triggers re-measurement. Robustness is continuous, not a point-in-time deliverable.

Complementary to inference-privacy and confidential-computing layers. Wand’s privacy layers protect the data path around the model. vlno changes the behavior of the model itself. Both layers are needed. Neither replaces the other.

Sovereign AI, on open models, hardened where it matters.

Read the joint announcement Talk to vlno