Your data is the moat. Your tuned models are your core intelligence. Hold your core, your premise, own your rack — hold your boundary. However, an AI rack isn't just another job runner. AI serving is a pipeline of data movement across physical and authority boundaries — compute, memory, fabric, storage. Nobody spans it today. And autonomous agents open a second surface no perimeter tool reaches.
Today's infra stack is yesterday's — it needs a new mindset and thinking.
This site is an early architecture atlas for design partners, architects, infrastructure leaders, and early adopters who want to pressure-test the system with us.
An AI rack isn't a job runner. AI serving is a pipeline across silicon, memory, fabric, storage — with branches and loops where the agentic workflow demands it. When it degrades, every point tool sees its slice. Nobody spans the pipeline end-to-end across boundaries.
The unit of work is the pipeline, not the GPU. Today's racks ship as a puzzle of bundled hardware or integrated for specific workloads; nothing spans them as a governed system for guaranteed workload outcomes. Every layer has its own dashboard; none owns the pipeline. AI work spreads quietly across on-prem, GPUaaS, and external LLM providers.
An enterprise AI workload isn't one shape. Agentic workflows branch and loop. MoE experts fan out. RAG retrieval hits vector and OLAP storage. Yet today's racks are still shaped like one big training job — same GPUs, same memory, same fabric, same lifecycle. The heterogeneous, stage-aware, lifecycle-decoupled rack architecture the enterprise needs doesn't exist as a shipped product.
Today's AI rack is many vendors at once — CPUs, GPUs, accelerators, NICs, BMCs, fabric switches, storage, firmware. The traditional path is rigid: qualify every combination, ship one tested release, freeze it. Every new component, every drawer swap, every patch reopens the test matrix — integration paid in months, refresh cycles measured in years.
An AI workload spans ingest, staging, compute, post-processing, persistence — across CPU, GPU, fabric, and storage. Every layer has its own dashboard; none owns the pipeline as a whole. No rack-level visibility, governance, or guardrails span the full path — downtime cascades before data reaches the GPU, and the symptom is rarely the root cause.
Employees spin up AI workflows, fine-tune models, and call LLM providers across on-prem, GPUaaS, and external APIs. Autonomous agents SSH switches, push config, shift traffic — with valid credentials, invisible to app-layer gateways. CIO and CISO cannot see who runs what, what data leaves the perimeter, or what an agent just did to the fabric.
Same pattern at every scale: stages plus feed, compute plus movement, work plus wait. Every stage depends on every other. The mistake every default abstraction layer makes is treating each stage as an isolated resource — a GPU, a pod, a VM — and hiding the stages from each other on principle.
VMware abstracted the hardware down. Kubernetes abstracted the cluster down. Both built layers of siblings that don't know each other exist. What's missing is a runtime that owns the substrate and the pipeline on top of it — as one system.
Anavec builds the AI infrastructure system for enterprise AI. AnaRack is the rack — heterogeneous, open, purpose-built for your premise. AnaROS is the runtime that runs on it — the AI-native OS that governs the pipeline. One system, engineered against itself. Deploy AnaROS software-first on your existing brownfield rack today; put AnaRack under it when the substrate matters. One design intent: you hold the moat — data, models, runtime — inside your own boundary.
Different from the mega DC. Built for the enterprise budget that can't refresh fleets the way hyperscalers do — and for the sovereign workload that can't leave the boundary. The substrate the runtime is engineered against.
The AI-native OS with authority over the substrate below it. Runs on AnaRack as the full system, or software-first on your existing brownfield rack — same K8s, same CUDA, no rewrites. Software-first deployment gets you the Pipeline Governor and self-driving engine on the hardware you already own.
The Pipeline Governor turns telemetry into action, on the rack — no SaaS pane, no external LLM, no egress. See. Decide. Act. Learn. One closed loop, running on the same substrate that runs your pipelines.
Your data is the moat. Your tuned models are your core intelligence. Anything that leaves your perimeter needs attention. AnaRack keeps the substrate in your rack. AnaROS keeps the runtime — telemetry, decisions, the whole operational loop — on that rack. No SaaS pane. No external LLM in the loop. The moat stays inside the boundary.
Bring AnaROS to the racks you already build. Pipeline Governor and self-driving loop on your fabric. Sovereign zones, air-gapped clusters — telemetry never leaves.
AnaROS governs placement and behavior on your GPUs — placement decisions with evidence, tenant isolation you can prove, no SaaS observability layer taking your telemetry off-net.
Drop AnaROS on your existing rack today. Pipeline visibility, workload governance, agent guardrails — no outbound telemetry stream, no rewrite. Land AnaRack under it when the substrate matters.
Air-gapped self-driving operation on AnaRack + AnaROS. The Pipeline Governor decides on your rack — no external LLM in the loop. Everything local.
Start at the rack. Walk to the runtime that governs it. See the workloads it lands. Choose your adoption path. Read in any order — every page is self-contained, and every page connects back to the same thing: rack + OS as one system.
All material confidential · NDA requiredThe hardware story. Open and heterogeneous across every layer — networking, CPU, multi-generation GPUs, memory, storage. Standards-based, no vendor lock-in. A new memory tier for memory-bound acceleration. Compute disaggregated from storage — data ready for AI before the GPU asks.
The runtime story. Above the device OS, below the agent. Pipeline Governor decides, self-driving operation acts — air-gapped, on your rack. Two surfaces on one backend: Telemetry (visibility) sees, Behavior (governance + guardrails) constrains. Five engines — Pipeline contracts, Placement engine, Live model, Stage telemetry, Change control — power the whole loop. Drops in on your existing stack. No SaaS.
The customer story. Real workloads from design partners — RAG retrieval, gigapixel inspection, multi-tenant serving, agentic pipelines. What changed when CPU:GPU ratio became a policy, and the pipeline governor took over.
The path story. Three ways to start: AnaROS-on-your-hardware (software-first), AnaROS-on-your-cloud (GPUaaS), or AnaROS-on-AnaRack (Anavec rack). One governance contract throughout. Step up or step back at any time.
Bring us a real workflow. We'll instrument it, profile it across the four layers of the stack, and propose a rack profile that scales independently along your real bottleneck.
We respond personally to every inquiry within two business days. We don't sell yet — we listen, and we shortlist.
Every atlas page is shared under mutual NDA. Most pilots land in 6–8 weeks.
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