Patent-pending AutoScale™ infrastructure products and hands-on engineering services — from GPU fleet management to production model serving.
Patent-pending infrastructure software that turns GPUs, GPU memory, storage, and inference throughput into dynamically managed, kernel-enforced utilities.
AutoScaleWorks.AI is building infrastructure software that enables organizations to manage GPU fleets across multiple hardware vendors through a single, unified control plane — eliminating vendor lock-in and dramatically improving GPU utilization at scale. Today, most organizations operate GPUs as coarse-grained, statically assigned resources with no real-time visibility into memory pressure, compute occupancy, or thermal state. The result is stranded capacity, idle GPUs still drawing full power, and overprovisioning that inflates both infrastructure cost and energy consumption. Our solution is cloud- and Kubernetes-agnostic and requires no modifications to existing drivers, runtimes, or applications.
Our technology provides kernel-level visibility that enables idle reclamation, thermal-aware migration, and intelligent multi-tenant scheduling — turning GPUs from static allocations into a dynamically managed utility that reduces both cost-per-inference and energy-per-workload.
Fine-grained VRAM management for multi-tenant AI infrastructure. Rather than statically partitioning GPU memory across workloads, our control plane enforces per-tenant VRAM budgets at the kernel level — tracking actual memory pressure in real time and reclaiming idle allocations automatically. KV cache reservation for LLM inference servers is managed as a first-class resource, preventing out-of-memory evictions and enabling predictable latency under concurrent load.
Intelligent, workload-aware storage orchestration that dynamically provisions, tiers, and migrates storage volumes based on real-time I/O patterns, latency requirements, and cost constraints. Our kernel-level I/O telemetry tracks read and write throughput per workload, enabling automated tiering decisions and eliminating storage overprovisioning across hybrid and multi-cloud AI infrastructure.
Kernel-enforced token rate limiting for LLM inference infrastructure. Our control plane enforces per-tenant token budgets directly in the kernel — applying rate caps at the system call boundary so that no single inference workload can exhaust shared GPU capacity. Token consumption is tracked in real time across concurrent model servers, with configurable per-request and per-second limits that apply without touching model code or inference runtimes.
Hands-on infrastructure engineering from GPU provisioning to production model serving and security automation.
Deploy and operate large language models on your own infrastructure. We build production vLLM clusters on GKE and OpenShift with GPU autoscaling, model weight caching, and OpenAI-compatible API endpoints.
Keep your data private, control your costs, and eliminate vendor lock-in. Our deployments serve Llama, Mistral, Qwen, and other open-weight models with enterprise-grade reliability.
Build intelligent retrieval-augmented generation pipelines that go beyond simple search. Our agentic RAG systems use tool-calling agents, persistent memory, and multi-step reasoning to answer complex queries over your data.
From vector database selection and embedding strategy to agent loop design and tool integration, we architect the full pipeline.
Intelligent surveillance systems that combine computer vision, natural language processing, and real-time alerting. Query your security footage in plain English and get instant, context-aware answers.
Our CV pipelines process camera feeds through multi-model detection (YOLO, Mask R-CNN), generate embeddings (CLIP) and captions (BLIP), and store everything in a searchable vector database.
Production Kubernetes clusters designed for AI workloads. We handle the entire infrastructure lifecycle: VPC networking, GPU node pools, IAM, storage, CI/CD, and monitoring.
Everything is codified in Terraform and Helm, version-controlled in Git, and deployed through automated pipelines. No manual kubectl required.
Flexible engagement models tailored to your needs.
Fixed-scope engagements with clear deliverables. Ideal for migrations, new deployments, and architecture reviews.
Ongoing support for your AI infrastructure. Monitoring, scaling, upgrades, and on-call incident response.
Architecture reviews, technology selection, and strategic guidance for your AI and infrastructure roadmap.
Tell us about your project and we'll scope the right engagement for your needs.
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