AI that runs where the data is made, with nothing sent anywhere.
The hardware arrived before the services did: phones and laptops now ship with neural processors that run small models at interactive speed, and the analysts expect AI PCs to be most new PCs this year. Almost nobody offers to put a working model on them. We do: distilled, quantised, evaluated, and shipped inside your app or your device.
Zingaro AI builds on-device and edge AI: small language, speech and vision models distilled and quantised to run on phones, laptops, NPUs and edge devices without a cloud connection, delivered as weights and runtimes inside the client's application or hardware, with a hybrid path to a larger model when a task needs one.
You get
- A model that runs on the device you name, at the latency, memory and battery budget you set, measured there.
- Weights, runtime and integration code delivered into your application, with the evaluation set.
- Privacy by construction: the data never leaves the device unless you route it.
- A hybrid path for the cases the small model should not handle alone.
Built with
- llama.cpp
- ONNX Runtime
- Core ML
- ExecuTorch
- TensorRT
- Qualcomm AI Engine
- Whisper-class speech models
- Distillation and quantisation toolchains
On-device and edge AI, end to end.
Model selection and distillation
Pick the smallest model that does the job, distil from a larger teacher where accuracy needs it, and prove it on your evaluation set.
Quantisation and compilation
INT8 and INT4 quantisation, pruning where it pays, and compilation for the target: Apple Neural Engine, Qualcomm, Intel and AMD NPUs, Android and iOS, embedded Linux.
On-device speech
Offline speech recognition and synthesis for apps and devices that cannot depend on a connection, in the languages your users speak.
On-device vision
Inspection, reading and detection models on cameras and edge boxes, where sending video to a cloud is too slow, too expensive or not allowed.
Hybrid routing
The device handles the common case; the uncertain case goes to a larger model on your servers or in your cloud, with the same evaluation gate.
Packaging and updates
Runtimes, model files and signed updates inside your app or firmware, with rollback, and memory and battery budgets measured on the real hardware.
From one painful job to a system in production.
Models and agents handle the volume. People handle the edges. You always know which did what.
- 01
Discover.
One or two weeks with your team. The jobs listed, sized and ranked. A number on the first one.
- 02
Build the pilot.
Fixed scope, fixed fee, 4 to 6 weeks. Real output on your real data, measured against the number.
- 03
Ship it.
Into production, inside your boundary if the rules require, with evals gating every release.
- 04
Run it, or hand it over.
We operate it with people on the queue and a weekly report, or your team takes it with the runbooks.
Your data does not have to leave the building.
On your servers
Air-gapped where required.
We install on machines you own, inside your network. Where the rules demand it, the system runs with no outbound connection and updates are carried in by hand. Your team keeps the keys.
Data stays inside your network
Your private cloud
We deploy into your account.
We deploy into your own cloud account, in your region, under your access controls. The data stays in your account. We get the access you grant, and nothing more.
Data stays inside your account
Ours
Managed, fastest to start.
We run it on infrastructure we operate. The right choice when the data is allowed to leave and you want the pilot running this month.
Managed by us, on our terms
In banking, insurance and healthcare, the rules decide where the data sits. We built for that first.
How each option worksWhat comes back, and how we measure it.
Deliverables
- A model that runs on the device you name, at the latency, memory and battery budget you set, measured there.
- Weights, runtime and integration code delivered into your application, with the evaluation set.
- Privacy by construction: the data never leaves the device unless you route it.
- A hybrid path for the cases the small model should not handle alone.
Measured by
- Accuracy on your evaluation set, on-device, against the cloud model it replaces
- Latency and throughput on the target hardware, not on a workstation
- Memory footprint, model size and battery draw per task
- Share of requests handled on-device versus routed
Real figures come from your pilot. We do not publish invented ones.
What people ask about on-device.
Which devices can Zingaro AI target?
Zingaro AI targets iOS and Android phones, Windows and Mac laptops with NPUs, and embedded Linux edge devices such as industrial cameras and gateways. Model size and quantisation are chosen for the specific chip, and every number is measured on that hardware.
How small can a useful model be?
For one narrow task, a model between a few hundred million and a few billion parameters, distilled from a larger teacher and quantised, is usually enough to match a hosted model on that task. Zingaro AI proves it on the client's evaluation set before anything ships, and routes the cases it cannot handle to a larger model.
Is on-device AI more private?
On-device AI is private by construction: the input is processed on the device and nothing is sent unless the application chooses to send it. Zingaro AI designs the hybrid path so that only the cases the client agrees to may leave the device, and logs when they do.
Bring us the on-device job you keep postponing.
Twenty minutes is enough to say whether we can take it.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences