Navigator
The design surface for building and configuring AI systems—assembling models, data connections, and logic into a deployable configuration without writing infrastructure code.
TECHNOLOGY FOUNDATION
The webAI partnership.
We evaluated the sovereign AI landscape and selected webAI as our platform foundation. The reason is architectural: webAI brings the model to the data rather than moving data to the model—which is the only structure that makes true sovereignty possible.
Official webAISystems integrator
20+ yearsSecure environments
Air-gappedDeployment capable
Zero exfiltrationData stays local
Fortune 100Operating experience
01 / THE ARCHITECTURAL IDEA
Bring intelligence to the data.
Conventional AI deployment inverts the security model most enterprises spent twenty years building. Data that is carefully governed at rest gets copied into a prompt and sent to infrastructure the organization does not own, govern, or audit.
webAI's platform is built the other way around. The model runs where the data already lives, under the access controls that already exist. Nothing sensitive has to move for the system to be useful, which means the sovereignty question is answered by the architecture rather than by a contract clause.
YOUR BOUNDARY
Data sourcesRecords, documents, telemetry
webAI runtimeLocal inference & tuning
Your usersAssistants & applications
Public egress: 0 — no prompts, context, or model weights cross the boundary.
02 / THE PLATFORM
A full local stack, not a hosted API.
Each component runs inside your environment. Forge deploys, integrates, and operates them as one system.
webFrame
The inference layer that makes local execution efficient, extracting performance from the hardware you own rather than assuming unlimited cloud capacity.
Runtime
Orchestration across nodes and devices—scheduling work, managing resources, and keeping the deployed system resilient in production.
Companion
The end-user assistant experience, grounded in your institutional knowledge and governed by your existing access policy.
03 / PERFORMANCE
Why local does not mean slower.
Entropy-weighted quantization
Model precision is reduced selectively rather than uniformly—preserving the weights that carry the most information while compressing the rest. The practical result is a substantially smaller model that retains its quality, so it fits and runs well on hardware you already have.
Adaptive inference
Computation is matched to the difficulty of the request instead of spending peak resources on every query. Straightforward requests resolve quickly; harder ones get the depth they need. Across a real workload this compounds into meaningful throughput and cost gains.
30%Smaller models through entropy-weighted quantization
2.6×Better performance per dollar on owned hardware
5–7×Faster inference with adaptive optimization
Figures published by webAI for the platform. Actual results vary with workload, model, and hardware; we validate expected performance against your specific environment during architecture assessment rather than asking you to take a benchmark on faith.
04 / HOW THE PARTNERSHIP WORKS
Clear division of responsibility.
webAI provides
- The sovereign AI platform and its security protocols
- Privacy-by-design architecture and local execution model
- Performance engineering across the inference stack
- Ongoing platform development and support
Forge provides
- Deep integration into complex enterprise environments
- Secure deployment methodology from high-security settings
- Architecture, hardening, and validation of the full system
- Operational ownership after go-live, and knowledge transfer to your team
One accountable partner for the deployment. You are not left integrating a platform on your own.
BEGIN INSIDE THE PERIMETER