Web for SaaS.
Edge Hardware for AI.
AiSpaceRiver builds purpose-built AI hardware, edge devices, and modern web platforms for SaaS products and platforms. We bridge silicon and software.
Our Mission
We exist to make advanced AI infrastructure and world-class web platforms accessible to every product team.
AI Hardware & Edge Devices
We design and manufacture energy-efficient AI hardware purpose-built for edge inference. From sensor nodes to autonomous systems, our boards are production-ready, secure, and scalable.
Web Development for SaaS
We architect and build modern web platforms for SaaS products. Performance-first, accessibility-driven, and designed to scale from MVP to millions of users without rewrites.
What We Do
End-to-end engineering for companies building at the intersection of hardware and software.
Custom AI Hardware Design
From concept to production — we design boards optimised for edge inference, low power, and real-world deployment.
Edge Device Development
Secure firmware, sensor integration, and connectivity stacks for IoT and edge devices across industries.
SaaS Web Platform Architecture
Full-stack web development for SaaS products — Next.js, APIs, auth, billing, and scalable infrastructure.
AI Model Deployment
Optimise and deploy models on edge hardware. We handle quantization, runtime integration, and performance tuning.
Prototyping & MVP
Rapid prototyping for hardware-software products. Go from idea to working prototype in weeks, not months.
Technology Advisory
Strategic guidance on hardware selection, architecture decisions, and build-vs-buy for AI and web projects.
Insights
Thoughts on AI hardware, edge computing, and SaaS web development.
Edge AI Inference at Scale: Architecture Patterns for Production
Dr. Elena Voss · June 5, 2026
Production edge AI requires a fundamentally different architecture than cloud inference. Here are the patterns that work.
Building the Ultimate AI Workstation for Deep Learning in 2026
Marcus Chen · May 28, 2026
The right workstation setup can cut your training iteration time in half. Here's what we recommend for deep learning in 2026.
Real-Time Computer Vision on Edge Devices: A Technical Deep Dive
Dr. Elena Voss · May 15, 2026
Achieving real-time computer vision on resource-constrained edge devices requires careful optimization across the entire pipeline.
Get in Touch
Have a project in mind? We'd love to hear from you.
Chicago, IL · Available for partnerships and consulting