AssistantKit, a Future Workshops product

Assistants that work on-device

AssistantKit is our kit for voice-first assistants that run entirely on your customer’s device. There is no cloud model, no API key and no per-conversation cost, and no data leaves the device.

Aeroplane mode on
Shopping Assistant On device
Somewhere quick for lunch near Tower 3? I’ve got 40 minutes.
Three places are within a six-minute walk and open now. The closest has seating inside.
[Restaurant A] Café · seating inside
2 min walk
[Restaurant B] Grill · takeaway
4 min walk
[Restaurant C] Salads · seating outside
6 min walk
Walking times and the map come from the same coordinates.
Listening
01 / 03

Private by construction.

The model, the routing, speech-to-text and the neural voice all run on the phone. With no server in the loop, customer data has nowhere to go.

02 / 03

Zero marginal cost.

There is no per-conversation bill. The ten-millionth question costs what the first one did, so usage growth stops being a finance risk.

03 / 03

Works offline.

The assistant keeps answering in aeroplane mode, in a basement or on a plane, because it has no network dependency.

01 · How it works

Five layers, one app.

Every step, from the first spoken word to the card on screen, runs inside the device.

Architecture

One core. Any number of apps.

The Apple Foundation Models adapter is the only code that knows which model is running, so the layers above it carry over unchanged.

On device Optional, uses the network
AppsONE KIT, MANY APPSCardsresult viewsGuardrailscontent rulesDatabundled, offlineToolsapp actionsRemote data storetransactions, needs networkConversation and cardsstreamed replies, a card per resultVoicespeech in, neural voice outApproval sheeta person confirms every actionruns the toolAssistantKit corepicks the tools for each turn, checks every call, keeps memory, screens contentApple Foundation Models adapterthe only code that knows the model; runs each turn on deviceOther modelsopen-weights, hostedApple platform, all on deviceon-device LLM, language, speech and audio, motion
Everything below runs on the device Network: not required
01

Listen

Raise the phone and speak. On-device speech-to-text turns the request into text.

02

Route

A two-stage hybrid router picks the right handful of tools from a large catalogue before the model is asked, at zero token cost.

03

Reason

The agent core runs the phone’s own model against only the tools that were routed to it.

04

Check

Schema, tool existence and permissions are validated. Anything destructive stops and asks first.

05

Show

Results render as UI cards, and the neural voice reads the answer back.

02 · For the technical evaluator

On-device still has to be right.

On-device still has to be correct, scalable, private, maintained and well designed. Small quantised models make that harder, so every output is checked before it runs.

Every model output, before it runs buyTicket()
PASS schema arguments match the tool signature
PASS exists tool is real and in the routed set
PASS permitted user has granted the access it needs
HOLD consent purchase: ask the user before executing
01

Validated output

Small models hallucinate arguments. Schema, tool existence and permissions are checked before anything executes.

02

Consent in the loop

Buying a ticket or booking a table pauses for the user’s confirmation inside the model’s own loop.

03

Deterministic safety

A rules layer that costs zero tokens, needs no network and is tuned per market. It keeps our Saudi deployment culturally correct.

04

Accessible by default

Every answer is spoken aloud and shown as a card. [Confirm specifics: VoiceOver, Dynamic Type.]