The leading AI compute platforms solve single-agent inference at scale.
AIR solves the layer above it — the one that makes AI agents work together as a coherent, production-grade workforce.
We built this. It runs. Let's talk.
NIM microservices, NeMo, Nemotron, NemoClaw. The world's most powerful compute stack, optimized for running individual AI models at unprecedented speed and scale. Single-agent blueprints. Developer tools. Infrastructure.
The substrate is solved. The factory is built.
The unsolved problem isn't running one agent fast. It's making 5, 8, 10 or more agents work together without failing — across roles, handoffs, error states, and real enterprise workflows. The size of the integrated agent team is continuously scalable to whatever the use case requires.
Single-agent solutions fail 73% of the time in production. AIR exists because teams work where solo agents break.
AIR doesn't compete with NIM or NeMo. It sits above them — the coordination brain that transforms individual inference calls into a coherent, reliable AI workforce.
GPUs, DGX, Blackwell. The physical substrate that makes AI fast, affordable, and scalable. This layer is solved.
NIM microservices, NeMo, Nemotron. Optimized model serving, fine-tuning, and single-agent deployment. Deployed across the ecosystem.
Role assignment, agent handoffs, state management, cross-agent verification, error recovery, workflow governance. The layer that makes AI teams reliable. This is AIR.
Every major enterprise AI platform is converging on the same problem: making agent teams reliable in production. The orchestration layer that solves this will become a strategic moat for whoever owns it.
Multi-agent coordination failure in enterprise production environments. The 73% deployment failure rate of single-agent systems. The absence of cross-agent verification and state coherence at scale.
Orchestration that scales to the agent-team size you need (5, 8, 10 or more). We start with a smaller agent set; scale grows with the deal. Our own AI agent pool (39 production-ready specialists) is what we use to run and demonstrate the system — available separately if required. 99.5%+ team uptime. Sub-2% error rate. Live enterprise deployments. Multi-model architecture across GPT-4, Claude, and Gemini optimized by agent role.
How agent roles are assigned, how handoffs are managed, and how the error recovery system maintains state coherence are disclosed only under a signed M&A-grade NDA.
The specific algorithms for task decomposition, agent assignment, and cross-agent verification are disclosed only in a controlled environment post-LOI.
We're offering a structured briefing to strategic partnerships or M&A teams at leading AI platforms. No pitch theater. One conversation, under NDA, about whether AIR completes your stack.