Enterprise AI Infrastructure

AI that runs inside
your infrastructure.

Production AI on hardware you own. On-premises, air-gapped, or in the cloud. Your data never leaves your network.

On-Prem • Air-Gapped • Cloud • Hybrid • Any LLM • Open Source

MLX Core running a local model, with chat and agent tools in one window
2 weeks
Deployment guarantee
Industry average: 6-12 months
Zero
Cloud dependencies
Fully air-gapped capable
14-day
Money-back guarantee
Scope agreed upfront

Enterprise AI is stuck

Data sovereignty isn't optional

HIPAA, CJIS, ITAR, and classified workloads can't touch external infrastructure. Most AI platforms require cloud connectivity by design.

AI projects stall for 6–12 months

Custom pipelines, new hires, orchestration built from scratch. Most projects never ship.

Vendor lock-in is a strategic risk

One provider sets your pricing and caps what you can do. Moving later means a rewrite.

AI without governance is a liability

No audit trail, no approval gates, no scoped permissions. Controls have to be built in, not bolted on.

Infrastructure-grade AI, inside your perimeter

Agents and skills are plain files

Skills are markdown with YAML frontmatter. Agents, MCP servers, and prompts are plain files on disk. Draft them in the composer or write them by hand. Either way, the files are the source of truth.

  • Skills are a short YAML header plus plain instructions
  • Named agents with their own model, tools, and workspace
  • Reviewed in pull requests, rolled back with a commit
  • No database, nothing hidden in a settings pane
An agent running its configured tools, each call showing an approval prompt

One engine, every modality

Text, images, video, music, speech, and 3D all come off the same server and the same model cache. There is no second stack to stand up, and no frames or audio go to a vendor.

  • Generate images, or edit a photo by describing the change
  • Video with a matching soundtrack in a single pass
  • Speech in dozens of voices, or a clone of your own
  • All of it reachable over the same HTTP API
Image generation running on local hardware, no cloud service involved

Works with your stack

OpenAI
Anthropic
Claude Code
Ollama API
Cursor
Zed
Continue
Open WebUI

Built for your security team to approve

You can watch it work

A live request monitor and full server logs, so you can see what ran and when. Retained audit records are an enterprise add-on.

Human approval gates

On high-stakes actions, the agent stops and waits for a person to say yes.

Infrastructure as code

Skills, agents, and prompts are version-controlled files. Deploy via CI/CD, review in PRs, roll back with git.

No Python, no supply chain

One code-signed binary. No interpreter, no package manager, no transitive dependencies to audit or patch.

You can check all of this yourself

MLX Serve is MIT licensed and developed in the open. The code, the release history, and the benchmark methodology are all on GitHub.

800+
GitHub stars
60 forks, tracked on Trendshift
12,000+
Release downloads
Across published builds
Weekly
Release cadence
Every version in the public changelog

The benchmarks are published, not asserted

Decode speed is measured against LM Studio, oMLX, and MTPLX on identical model weights, and every run is committed to the repository with the harness and the method. On shipping defaults the engine averages about 26% faster than LM Studio. On some models the gap is far wider, and on a few it is a tie. All of it is in the table, including the rows where we do not win.

Read the benchmark results or see the engine site.

What we are working on right now

Active pilot in public sector critical infrastructure

We are running a pilot with a public sector critical infrastructure operator. The question is whether local AI can read operational log data and produce a root cause analysis worth trusting. The logs never leave their network. They are indexed and queried on hardware the agency controls, and nothing goes to a model vendor.

This is an evaluation, not a finished deployment, and we are not claiming a result yet. We will publish what we learn when it wraps up, including if the answer turns out to be no.

From scoping to production in two weeks

Deploy inside the perimeter

A code-signed native app on hardware you own, air-gapped if required, with zero external dependencies.

Built-in web console

Chat playground, live request monitor, image and audio tools, and the API reference. All on the same port.

MCP protocol & open APIs

Full MCP client support, four APIs on one port, streaming, and tasks you schedule in plain English.

Publish agents to users

We put agents behind your own auth, rate limits, and logging, so they are safe to hand to people outside engineering.

2-Week Money-Back Guarantee

We define success criteria together. If we don't deliver a working AI workflow within two weeks, you get your money back.

Book a Demo
Terms and conditions apply. Scope must be mutually agreed upon before engagement begins.