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The hard part isn’t generating intelligence. It’s operationalizing it.
Powerful models are everywhere now. The real challenge is turning them into systems organizations that can actually deploy, govern, and scale. That’s where friction appears.
Teams end up managing multiple APIs, integrating models across providers, dealing with inconsistent latency, and trying to control rising inference costs, all while meeting enterprise requirements around compliance, data protection, observability, access control, and operational reliability.
Most organizations end up making a tradeoff: move fast and accumulate governance debt, or lock everything down and lose the speed that made AI valuable in the first place.
Compass removes that tradeoff by combining unified access to leading AI models across text, vision, speech, and embeddings with built-in security & sovereignty controls and multi-accelerator architecture. Organizations can run workloads on the silicon best suited for their performance and cost requirements across NVIDIA, AMD, and Cerebras infrastructure while maintaining enterprise-grade security, in-country data residency, and zero customer data logging. As the AI landscape evolves, adopting a better model becomes a routing decision, not a platform rebuild.
Serving intelligence efficiently at scale.
The next phase of AI will be defined by inference. Once AI moves into production, the economics change completely. The question is no longer how large the model is. It’s how efficiently intelligence can be served at scale.
Most inference stacks were built as extensions of training infrastructure. They weren’t designed for high-concurrency enterprise workloads or cost efficiency across different accelerator architectures. As concurrency rises, latency becomes unpredictable, time-to-first-token slows down, and expensive accelerators end up handling workloads that don’t require premium hardware.
Core42 Compass was architected differently. A core differentiator of Compass is its built-in multi-accelerator inference architecture. Instead of forcing every workload onto a single silicon stack, Compass enables organizations to run workloads across NVIDIA, AMD, and Cerebras infrastructure based on their performance and economic requirements.
That flexibility matters because different AI workloads behave differently. Some require ultra-fast real-time inference. Others demand throughput at scale. Others are optimized around cost efficiency.
Compass combines Cerebras, NVIDIA, and AMD infrastructure to support those varying requirements while maintaining enterprise-grade security, performance and latency SLAs. For example, ultra-fast inference powered by Cerebras delivers industry-leading time-to-first-token and up to 20x throughput improvements for workloads that depend on real-time responsiveness.
The platform supports tens of billions of production tokens weekly with enterprise-grade availability built directly into the inference layer.
Instead of overpaying for every request or locking workloads to a single accelerator architecture, organizations can match the right model and the right silicon to the right workload.
That’s what serving intelligence efficiently at scale actually looks like.
From prototype to production without rebuilding the stack.
In most organizations, experimentation and deployment live in two different worlds. That gap is where momentum dies. Compass closes it with a single platform that takes AI systems from first prompt to enterprise-scale deployment.
Teams can experiment in the Compass Playground, compare models, evaluate outputs, benchmark latency, and move directly into production-grade inference, fine-tuning, agentic workflows, and enterprise integrations without rebuilding the environment halfway through the lifecycle. No fragmented tooling. No painful replatforming between experimentation and deployment.
Sovereignty built into the platform, not layered on afterward.
This is where Compass genuinely separates itself. Speed without control creates governance debt. Compass embeds sovereignty and oversight directly into the inference layer.
Sovereign deployment with in-country data residency means GenAI applications operate under national regulatory requirements without slowing development. Sensitive data stays where it must.
Enterprise-grade governance brings private endpoints, access controls, guardrails, and authentication and authorization mechanisms aligned to regulated environments.
Operational and cost visibility through metering, billing, and resource management lets you track usage across applications and teams, manage budgets proactively, and scale sustainably instead of being surprised by the bill.
The result is a platform that delivers startup-level speed with enterprise-grade trust.
Operationalized intelligence.
The organizations leading the next wave of AI adoption won’t simply have access to better models. They’ll have infrastructure capable of turning intelligence into a reliable operational capability: secure, governed, scalable, and continuously adaptable as the market evolves.
That requires more than model access. It requires purpose-built infrastructure for production AI. Core42 Compass is the operational layer for enterprise AI, combining frontier model access, multi-silicon infrastructure, sovereignty, and governance into a platform built for intelligence production.
See what Compass can do for your organization.
Explore the Compass platform, or talk to our team about a sovereign, production-ready deployment built for your requirements.