// what we build

I design and ship production-grade local AI stacks: the models, the serving layer, the voice pipeline, and the agents that tie it together. The goal is a system your team actually uses — not a demo that dies in a notebook. Everything below runs offline, from air-gapped to fully on-premise.

Reference stack
Qwen 27B · full precisionServed locally at BF16 (full precision) — not a quantized approximation
Compute
Clustered NVIDIA RTX GPUs + Apple SiliconMulti-GPU inference (e.g. 4× RTX 3090) and Apple M-Ultra Mac Studio for CPU/Neural Engine workloads
Voice
Local TTS + STTText-to-speech and speech-to-transcription that never leave the network — voice in, voice out, air-gapped
Agents
Agentic infrastructureHermes Agent + VS Code tooling, memory, tool use, and evals — the same stack that runs an autonomous marketing agency

// who it's for

Three teams get the most out of running AI on their own hardware. If you're one of them, this is the engagement.

privacy-critical

Businesses that can't let data leave the building

Regulated industries, IP-heavy work, and any org where "no data leaves your network" is a hard requirement — what people call air-gapped AI.

  • No connectivity to the internet at runtime — the model runs entirely on-premise
  • Full-precision local models tuned to your data and use case
  • Local voice pipeline: speak to it, it speaks back, nothing recorded to a cloud
→ outcome: enterprise-grade AI capability with the privacy posture of a disconnected system.
marketing agencies

Agencies that want AI efficiency without the lock-in

I run an almost-autonomous marketing agency on this exact stack. If you're a traditional agency watching the gap open up, I show you how to close it — and help you build the setup, not just talk about it.

  • See the reference implementation: content, SEO, lead capture, and reporting running on local AI
  • Get a working setup your team can adopt — not a slide deck
  • Understand where AI actually pays back in an agency workflow, and where it doesn't
→ outcome: an agency that moves faster and keeps its data — and its margin.
software shops

Dev shops that can't afford third-party providers

Cloud LLM APIs add per-token cost, vendor dependency, and a data-egress you may not be allowed to have. Going local is cheaper at scale, faster at the edge, and keeps client data on your side of the line.

  • A production serving setup your team can start using on day one
  • Hardware + model selection matched to your budget and workload
  • Full-precision local inference — no quantization quality loss, no monthly token bill
→ outcome: AI capability without the compromise — cost, privacy, or vendor lock-in.

// what you get

A scoped engagement that ends with a running system, documented and handed over to your team.

$ ./local --air-gapped

Ready to put AI on your own hardware? Tell me your use case and privacy constraints — I'll size the setup and scope the engagement.

Book a Local AI Session