Production discipline for AI infrastructure.
We're an engineering agency that helps teams run AI in the real world. Our work is the platform layer: the CI/CD, Kubernetes, observability, and security that turn models into dependable services across cloud, hybrid, and edge.
Why we started this
AI teams are brilliant at building models and prototypes, but the gap between a working notebook and a reliable production system is where most projects stall. That gap is exactly where a platform engineering team earns its keep.
We started this agency because that gap keeps showing up the same way: GPU clusters without scheduling discipline, RAG demos without ingestion pipelines, edge pilots without update or monitoring strategy. The pattern is consistent: strong AI work, fragile operations underneath it.
We're also genuinely obsessed with the hardware: Raspberry Pi, NVIDIA Jetson, DGX Spark, and the new wave of edge AI devices. That curiosity became a specialty: bringing the same rigor from cloud platforms down to fleets of devices in the field.
We don't pretend to be an application shop or an ML research lab. We're the team that makes their work run in production, and we partner with specialists when a project needs app or model expertise.
Our principles
Production over prototypes
Demos are easy; running AI reliably is not. We focus on the operational layer that keeps systems up, observable, and affordable.
Open standards, no lock-in
We lead with open-source and portable patterns so you keep control of your data, keys, and exit strategy.
Everything as code
Infrastructure, deployments, and policy live in Git. Repeatable, reviewable, and auditable by default.
From cloud to the edge
The same discipline applies whether workloads run in a datacenter GPU cluster or on a Jetson in the field.
years running production infrastructure
first: everything as code
ready: cloud to device
data, keys & stack, always
Platform engineering across the AI stack.
Three disciplines, one engineering standard, whether you start with cloud platforms, retrieval infrastructure, or edge fleets.
AI platform engineering
Kubernetes platforms, GPU scheduling, model serving, and GitOps promotion paths, built for teams shipping models to production.
RAG & agent infrastructure
Vector stores, embedding pipelines, gateways, and observability for retrieval and agent systems, without owning your application layer.
Edge inference operations
Fleet provisioning, OTA updates, and remote ops for devices in the field, integrated with your cloud and hybrid stack.
Who we work with
- AI or platform teams with models ready (or nearly ready) for production
- Engineering leaders who need GPU, serving, or edge infrastructure stood up properly
- Organizations that want open standards, not a single-vendor platform bet
- Teams stretched thin between research velocity and operational maturity
Better with a partner when…
We're clear about scope. These are common cases where we're not the right primary team, and we're happy to collaborate instead.
- Training foundation models or building consumer AI apps end-to-end
- One-off scripts with no path to repeatable operations
- Projects that need 24/7 NOC coverage without an engineering partnership
Assess, build, then operate.
A straightforward engagement model: start with clarity, deliver the platform, and stay on as long as you need us.
Assess
We review your current stack, constraints, and production goals, then deliver a clear, prioritized roadmap.
Build
We stand up the platform layer: clusters, pipelines, observability, security, and the runbooks your team can own.
Operate
Optional ongoing partnership for improvements, incident support, capacity planning, and platform evolution.
Ready to take your AI workloads to production?
Let's talk about your platform: cloud, hybrid, or edge. Start with a short, no-pressure conversation.