Build Intelligent Systems.
Deploy Real AI.
We design, build, and deploy production-grade AI agents and intelligent systems for enterprises that need more than a proof of concept. From computer vision pipelines to autonomous robotics, we turn complex AI into operational reality.
Try asking our sizing advisor: “What GPU do I need to serve a 70B model at low latency?”
Premier Applications
Production applications running on GPU Clouds infrastructure that you can use today.
prouter.io AI Gateway
Stop paying cloud AI prices for every prompt. prouter.io routes simple work to local GPUs and escalates only the hard tasks to premium cloud models.
- One API key for chat, image, video, and voice
- Local-first routing to reduce unnecessary cloud spend
- Automatic handoff from low-cost to premium models
- Credit-based usage tracking across all workloads
- Signed artifact handoff to marketing workflows
- Admin visibility into cost, artifacts, retries, and delivery
- Built for private GPU clusters with cloud fallback
reAIagents
Drop a URL. Get your marketing gaps in 60 seconds — free. Then let AI run your whole marketing team, with you approving every move.
- Free 60-second website scan — instant gaps & quick wins
- Brand DNA auto-extracted from your URL, no questionnaire
- 49 AI marketing experts, grounded in your brand
- Full campaign pipeline — human approval before anything goes live
- Reuse-before-regenerate creative engine saves time & spend
- Self-optimizing ad performance loop
- Free retention engine — your own email provider, no lock-in
No credit card required to start
How do I size my AI solution?
Not sure what hardware you need? Our AI advisor analyzes your workload and recommends the right GPU, CPU, or NPU configuration -- cloud or on-prem.
Free to use. No login required.
What does GPU Clouds build?
End-to-end AI systems across five core domains, engineered for production environments and enterprise-grade reliability.
AI Agents
Autonomous agents that handle complex workflows, from customer support triage to document processing and decision-making pipelines. Built with modern agent frameworks, structured workflows, retrieval, tool use, and custom orchestration.
Computer Vision
Real-time object detection, quality inspection, medical imaging analysis, and video analytics. Optimized for low-latency edge or cloud deployment.
Robotics
Intelligent control systems for industrial robots, warehouse automation, and autonomous navigation. ROS2 integration, SLAM, path planning, and real-time sensor fusion.
Full-Stack AI Platforms
Complete platform builds integrating multiple AI capabilities into unified dashboards, APIs, and workflows. From data ingestion to model serving and monitoring.
Enterprise AI Consulting
Strategic AI roadmapping, architecture reviews, and implementation planning for organizations ready to move from experimentation to production AI at scale.
How does an AI build work?
A proven three-phase process that takes you from idea to deployed AI system, with transparency at every step.
Describe Your Challenge
Tell us the problem you need to solve. We listen carefully, ask the right questions, and map out the technical landscape. No jargon, no upsell -- just a clear understanding of what success looks like for your business.
We Plan the System
Our engineering team designs the architecture, selects the right models and tools, and creates a detailed implementation plan with milestones, timelines, and cost breakdowns. You approve before we write a single line of code.
We Build and Deploy
We build iteratively with weekly demos, rigorous testing, and production hardening. You get a fully deployed system with monitoring, documentation, and ongoing support. No prototypes left on a shelf.
What outcomes do these AI systems deliver?
Representative project outcomes from our engineering work. Client names withheld where required.
Automated Visual Quality Inspection
Replaced manual inspection of precision components with a real-time computer vision system. Detection models running on edge devices identify defects at line speed, reducing scrap rates and eliminating inspection bottlenecks.
AI-Powered Adaptive Learning Platform
Built an intelligent tutoring system that adapts to each student's learning pace and style. LLM-driven content generation, real-time assessment, and personalized learning paths -- deployed across a network of training centers.
Multi-Agent Document Processing Pipeline
Deployed an autonomous agent system that ingests, classifies, extracts, and routes thousands of documents daily. Reduced a multi-person manual process to a single human-in-the-loop review step with full audit trails.
How do we work with you?
Start small and low-risk. Most engagements begin with a free conversation — no commitment, no obligation.
Free sizing & discovery call
Use the AI advisor to size your workload, then talk it through with an engineer. We’ll tell you honestly what it takes.
Free · no commitmentScoped architecture sprint
A short, fixed-scope engagement that turns your requirement into a concrete architecture, plan, and cost.
Short · fixed-scopeBuild & deploy
We build, test, and deploy the system to production on dedicated GPU infrastructure — cloud, on-prem, or hybrid.
Typical build: 6–10 weeksFrequently Asked Questions
Straight answers on scope, timelines, hardware, and how an engagement starts.
What kinds of AI systems does GPU Clouds build?
We build five kinds of production AI systems: autonomous AI agents for complex workflows, computer vision for detection and inspection, robotics integration, full-stack AI platforms from ingestion through model serving, and enterprise AI consulting. Every engagement targets a deployed, monitored system in production rather than a prototype.
How long does it take to build and deploy an AI system?
A typical build runs 6 to 10 weeks from approved architecture to a deployed, monitored system. Before that, a short fixed-scope architecture sprint turns your requirement into a concrete architecture, plan, and cost. We build iteratively with weekly demos, testing, and production hardening.
What GPU do I need to run a large language model?
It depends on model size, latency target, and how many concurrent users you serve. Our free GPU Sizing Advisor takes a plain description of your workload and recommends a GPU, CPU, or NPU configuration, including whether cloud, on-premise, or hybrid makes sense and whether you need dedicated, shared, or serverless capacity. No login is required.
Do you deploy on cloud, on-premise, or hybrid infrastructure?
All three. We deploy to dedicated GPU infrastructure in the cloud, on-premise in your own data centre, or a hybrid of the two, depending on your data residency, latency, and cost constraints.
How does an engagement start, and what does it cost to begin?
It starts with a free sizing and discovery call, with no commitment or obligation. You can size your workload with the AI advisor first, then talk it through with an engineer who will tell you honestly what the build takes. Paid work begins only at the fixed-scope architecture sprint, after you approve the plan.
What does a production AI system actually cost to run?
Cost breaks into three parts: model usage, build and integration, and ongoing operations and iteration. The third is the one most teams underestimate. We publish a detailed 2026 breakdown of what each part costs for a small or mid-sized business, including when self-hosting starts to beat per-token API pricing.
Where is GPU Clouds based?
GPU Clouds is a B2B AI engineering firm and agent marketplace based in Pennsylvania, USA. We work with clients across education, healthcare, manufacturing, infrastructure, and enterprise operations.
Turn Your AI Requirement Into a Build Plan
Whether you have a clear specification or just an idea, we can help you turn it into a production AI system. Describe your use case and get a structured project plan.