Intelligence
you own.
Runs on your hardware.
Learns from you.
Keeps everything it learns
and builds everything you need.
You are the best training data there is. Right now you're training someone else's model. Kaba keeps it yours.
One place to run every model.
Browser, terminal, files, and models in a single harness on your devices. Kaba does the work itself, in the same windows you do. Policy checks every step before it runs.
It remembers what matters to you.
Everything you see and do becomes private, encrypted memory, curated into clean datasets automatically.
Then it gets better at it.
Train expert models on your own context, on your own GPUs or a peer’s. Every version is evaluated before it ships.
A loop that runs continuously, stays on your hardware, and compounds with every task.
Intelligence off.
Everything else on.
Models and memory are one part of Kaba, and every bit of it can be switched off. What stays is the best way you have ever used the internet: private, peer to peer, and built with features the big browsers will never ship.
You say yes. Or nothing happens.
Anything sensitive waits for your approval. Approve it and Kaba learns from your choice. Deny it and nothing leaves your machine.
Which Kaba is for you?
Your hardware.
Your model.
Be your own lab.
Your laptop, desktop, and home lab become one private intelligence that learns from how you work. Build datasets from your own context, train LoRA experts on your own hardware, and own every one of them. Local and encrypted by default.
The client is the lab
The client docs ↗Using models is now the norm. With Kaba, build datasets, train with RL, and turn telemetry into fuel. Be your own lab.
How Kaba works
Protocols docs ↗Marketplace — an open market for experts
Marketplace docs ↗Mobile — your cluster in your pocket
Mobile docs ↗kabactl — the whole mesh from one prompt
Kabactl docs ↗
Built on
Internals docs ↗How Kaba compares
Open-source harnesses and local AI tools, compared on what Kaba adds: memory from your own activity, training on it, policy on every step, and a private mesh across your devices.
Your hardware. Your model.
Start training your attention.
Today, prompts, files, and the judgment of your best people leave through tools nobody approved.
Intelligence your company controls,
end to end.
Put intelligence to work on your own infrastructure, with your people, ontology, policies, and expertise in every loop. Accelerate adoption without turning speed into insider risk.
Every team is already putting intelligence to work. The question is whether the company can see it, govern it, and keep what it learns.
Without a shared foundation, each new tool brings its own data copies, its own connectors, its own bill, and its own blind spots. Adding the next one never gets easier. A harness gives every model, workflow, and team the same ground to stand on, so the tenth deployment costs less than the first.
See it on your infrastructure, with your policies and approvers in place from day one.
Super Intelligence (SI), on hardware you control.
Government requirements shaped Kaba from the start. Data sovereignty, classification boundaries, and full auditability were design inputs, so agencies can run Super Intelligence (SI) under their own authority.
Kaba runs 100% air-gapped. Models, data, and training stay on government hardware, with no outside connection required, and every run is governed by policy, audited, and accounted for.
Kaba is written in Rust, a memory-safe language, and follows the memory safety guidance published by NSA and CISA for software used across the Department of Defense and federal government.
An operating system for intelligence.
Central control. Distributed compute.
An operating system decides what runs, what it can access, and how it talks to the outside world. Kaba does the same for intelligence across every device in the fleet, through the harness and its private network.
Intelligence makes thousands of small decisions, and every round trip to a remote server slows each one down. So nothing is virtualized and no screen is streamed: work runs natively on each device, or the nearest peer in your mesh, while IT keeps enrollment, central policy, access rules, updates, audit, and remote wipe of keys and data.
Enterprise in the loop
Enterprise docs ↗Every deployment runs through three loops: your people and ontology, your policies, and your expertise. They are built into the runtime, not sold as add-ons.
Run the whole fleet from one place
Every laptop, workstation, server, and edge device running Kaba is part of one fleet. Manage it, search it, and train across it, with every result feeding the company model.
A model that builds itself from your people
Each team trains adapters on its own work. Kaba routes across them as a mixture of experts, so the company model improves every time someone does their job well.
Speed without understanding is an insider threat
Security docs ↗The fastest way to adopt intelligence is to let everyone use it. When nobody understands where data goes, what automation can touch, or which models learn from it, that speed becomes the biggest risk inside your company. Kaba lets you accelerate with the risk visible and governed from the first deployment.
You can't control the cost of intelligence until you control its use
For most companies, spend on intelligence is growing faster than anyone can track. Seats, API tokens, and cloud GPUs are billed by different vendors to different teams.
Cost and risk are the same problem. When you can't see who is using which model on what data, you can't secure it and you can't budget for it. Kaba puts use under control first, and cost control follows.
How Kaba Enterprise compares
Compared with platforms enterprises evaluate for governed intelligence. Prisma AIRS secures AI traffic and can run alongside Kaba; Palantir AIP grounds models in a central data platform; Microsoft 365 Copilot runs in Microsoft's cloud.
See it on your infrastructure, with your policies and approvers in place from day one.
Your hardware. Your model.
Start training your attention.
Security people who don't want to slow you down.
We've spent decades in enterprise security. We've also watched security teams become the reason new technology stalls.
We don't think that trade-off is necessary. You can move fast, understand your risk, and protect your data at the same time. Kaba is how we're proving it.
Our team has worked incident response at GE CIRT and done R&D at Mandiant. Our founder started Threat Stack, an early cloud and Linux security company built on deep syscall monitoring and acquired by F5, and Critical Stack, a secure Kubernetes platform acquired by Capital One, and co-founded Kolide, the endpoint monitoring company built with the team behind osquery and acquired by 1Password. Kaba is the latest of those, and not the last.