Go, Rust, Kubernetes & AI Systems Engineering | Ardan Labs

Instructor: Herbert Wolverson
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Learn how we've helped teams build scalable platforms using Go, Rust, and Kubernetes.
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We architect, build, and scale production systems with the engineering discipline that has defined Ardan Labs since 2010.
Four integrated services. One goal: helping you build better systems that perform in production.
Consulting - Turn complex technical challenges into clear architectural decisions and actionable engineering plans.
Learn about Consulting
Training - Build the engineering capability to maintain, scale, and evolve your systems long after the engagement.
Staff Augmentation - Add experienced engineers who integrate directly into your team and contribute from day one.
Learn about Staff Augmentation
AI Engineering - Apply systems engineering principles to AI infrastructure, data, models, tooling, and production deployment.
Explore AI Engineering
Trusted by teams across industries:
Retail, Pharmaceutical, Logistic & Supply Chains, Finance, Events, Cybersecurity, Manufacturing, Energy, Cyber Warfare, and Gaming.
Open Source AI Tooling
A Go based SDK for local AI inference that explores a simpler approach to building AI systems with fewer moving parts.
“ After speaking with William Kennedy and Florin Pățan at GopherCon, I am more convinced that local models are the way forward and that Kronk is a great tool to enable that for a lot of teams.
Principal Engineer, Bayer Crop Science
Local speech-to-text for Go
Whisper.cpp bindings with pure-Go audio decoding. No CGo. No cloud.
Local image generation for Go
Stable Diffusion bindings with pure-Go PNG and JPEG I/O. No CGo. No cloud.
Fine-tuning models for Go
Local model fine-tuning in the Kronk ecosystem. Train and adapt models without leaving your stack.
AI Engineering for Real Systems
AI is becoming part of the production stack. Building it well requires more than connecting an API.
Ardan Labs applies decades of systems engineering experience to AI strategy, infrastructure, data, inference, tooling, and deployment.
Identify valuable use cases and design systems around real technical constraints.
Build reliable data pipelines, retrieval systems, vector search, and RAG architectures.
Evaluate, integrate, optimize, fine tune, and deploy models where they make sense.
Build agentic workflows, internal tools, observability, deployment pipelines, and production infrastructure.
We bring systems thinking, production experience, technical depth, and knowledge transfer to every engagement.
We look beyond individual features to the architecture, dependencies, performance, and operational realities surrounding them.
Our recommendations come from engineers who build, operate, and troubleshoot real systems.
Go, Rust, Kubernetes, distributed systems, performance, cloud infrastructure, and AI.
We don't just build alongside your team. We help your engineers understand what we're building and why.
Carnegie Technologies
See how we help teams solve production problems in Go, Rust, Kubernetes, and AI.