Some data cannot go to a provider, and some products cannot depend on a network round trip. Running the model on the device solves both, and introduces its own problems: quantization without wrecking quality, memory budgets on hardware you do not control, and shipping a model inside a signed, notarized build. We have done this in production, including transcription clean enough to sync straight to a CRM.
01 / ON_DEVICE
For data that cannot leave the machine. Quantized models inside signed desktop builds, with the latency and memory work that makes them usable.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
On AnyTeam the transcription runs entirely on the rep's laptop and is clean enough to sync straight to a CRM. On-device work fails on packaging and thermals far more often than on model quality.
A quantized model that lost ten percent on your task is a bug you will find in production. We measure against your eval set at each size before choosing.
Shipping a model inside a desktop application is a distribution problem as much as an ML one, and it is where most on-device projects stall.
Week 1
Memory, thermal and latency limits on the machines your users actually have, not on a workstation. The budget decides the model, not the other way round.
Week 1-3
Every size reduction costs quality somewhere. We measure where, so the trade is chosen rather than discovered after release.
Week 3-5
Embedded in a signed, notarized application, with the packaging and update path that implies.
Week 4-6
Which requests stay local and which may go to a provider, and what happens when the network is gone.
SalesIn production
A real-time meeting-intelligence stack for account executives. It detects a live meeting, transcribes it on-device, guides the rep during the call, and writes a structured, CRM-ready summary the moment it ends, with no audio ever leaving the laptop.
Executive operationsIn production
A 24/7 AI chief-of-staff for founders and leadership teams. An agent that plans, acts, and self-corrects across email, calendar, docs, CRM, and ERP through a secure tool layer, runs recurring workflows end to end, and never exposes a credential.
A capability check decides whether a device can run the model at all, and if it cannot it falls back to a smaller one rather than silently uploading the data — the failure that quietly defeats the entire point. The bundle is quantised and runs against a local store, and the answer never leaves. Updates travel the other way: a signed build whose origin can be proved, released to a staged cohort before the rest of the fleet. Telemetry is counts, not content, so you learn how the model behaved without learning what anyone asked it.
Production-proven
Built by engineers who've already shipped this in production.
For narrow tasks, frequently yes, and the eval set answers it in the first fortnight rather than after the build. For open-ended reasoning, usually not, which is what the hybrid line is for.
We set the budget against your actual install base. That constraint drives model choice, rather than picking a model and hoping.
Through the update path we build, separate from application releases where that makes sense, so a model improvement does not require a full reinstall.
Yes, and that is usually the right design. Sensitive or offline requests stay local; the rest can route out when the network allows.
A data boundary in front of the model, for teams who cannot send raw records to a provider.
View serviceThe product surface around your model, for teams who have the AI but not the app.
View serviceOpen models on your own infrastructure, for teams who will not call an API.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.