On-device inference core
Quantized language and speech models running locally on modern mobile hardware, so core assistant features work offline and never ship sensitive context to a server.
AI-Built Apple Intelligence Clone
Apple Intelligence-Style AI Assistant Development
An AI-first build of the on-device assistant pattern: private context that summarizes notifications and documents locally, intent routing that turns requests into system actions, and fallbacks to cloud models only where privacy policy allows. Four synced panels ship white-label with 100% source code, owned by you from day one. The almost-ready AI build goes live in 6 working days; a custom build takes 3 to 4 weeks.
Launch-ready from
$2,799
- Live in 6 working days
- 60 days support
- Complete source codes
- Complete rebranding
- White-labeling
- 1 year free updates
AI engineers
Almost-ready AI launch
Custom build
Source-code ownership
Trusted by the brands we build for
Moxace AI platforms by the numbers
Canonical facts, not promises.
- /012010Operating Since15+ years shipping apps
- /029,000+Projects Deliveredacross 35+ industries
- /033,900+Apps Publishedin 25+ countries
- /04120+AI Products in Production98% model accuracy
overview
What is an apple Intelligence-style AI assistant?
An Apple-Intelligence-style assistant platform is a privacy-first AI layer built into a product: on-device models handle summarization, rewriting, and notification triage locally, a private context index lets the assistant reason over the user's own documents and messages, and an intent system maps natural language onto real actions inside the app and OS surfaces where the platform permits.
The business problem it solves is AI without the privacy trade. Enterprises, device makers, and vertical software vendors want assistant features their users actually trust, and a build where sensitive context stays on-device, with cloud fallback as an explicit, governed path, is the version that clears enterprise security review.
A complete build includes the assistant app and system surfaces, an on-device inference layer with quantized models, a private context index, an intent and action framework, and an admin console over model routing, fallback policy, and telemetry that never includes raw user content.
Market context, hedged: on-device AI is a fast-moving frontier where platform capabilities change quarterly. We make no claims about market size; the durable signal is that privacy regulation and enterprise procurement both keep pushing assistant workloads toward local inference, and few teams can engineer that layer well.
Main features of our apple Intelligence-style platform
The headline capabilities, at product level.
Private context index
A local, encrypted index over the user's documents, notes, and messages that the assistant reasons over, with nothing leaving the device unless policy allows.
Notification and mail summarization
Priority-sorted notification digests and thread summaries generated on-device, so attention is triaged before the user opens a single app.
Writing tools
Rewrite, proofread, and tone-adjust actions available system-wide through share targets, tuned to your product's voice.
Intent routing and actions
Natural-language requests mapped onto structured intents that trigger real app actions, with confirmation gates on anything destructive.
Cloud fallback governance
Requests above local capability escalate to cloud models only where your privacy policy permits, with per-feature routing controlled from the admin console.
Siri and Shortcut-style triggers
Voice, text, and automation triggers feeding the same intent system, so the assistant is reachable from anywhere in the product.
Private telemetry
Usage and quality signals collected with differential-privacy style aggregation, never raw content, so quality improves without surveillance.
Who should launch an apple Intelligence-style assistant?
What these buyers share: users who will not hand over their context to a third-party cloud, and products that need AI built in rather than bolted on.
Enterprise software vendors
Adding assistants that pass security review because the default path is on-device. The AI core ships pre-tuned while your team keeps the roadmap.
Device and hardware makers
Shipping an assistant layer on your own hardware, with inference budgets tuned to your silicon rather than a cloud vendor's margins.
Vertical SaaS in regulated fields
Health, legal, and finance products where client context cannot leave the premises, and assistant features still win the deal.
Messaging and productivity apps
Summarization, drafting, and triage built into the flows your users already live in, under your own brand.
Agencies and innovation teams
Deploying a branded assistant platform for clients who need an AI story that survives a privacy audit, on 100% owned source code.
Feature panels of your AI assistant platform
Four products operating as one system. Every panel ships in every build.
Assistant app
The surface users talk to.
- ✓Text and voice input
- ✓Conversation history stored locally
- ✓Writing tools in share sheet
- ✓Notification digest view
- ✓Document Q&A over private context
- ✓Action confirmations
- ✓Offline-capable core features
- ✓Model status and privacy indicators
- ✓Cross-device sync where permitted
- ✓Feedback capture on answers
Context and intent layer
The reasoning engine under the surface.
- ✓Encrypted local context index
- ✓Permission-scoped data sources
- ✓Intent catalog with parameters
- ✓Confirmation gates on risky actions
- ✓Tool and plugin registry
- ✓Fallback routing rules
- ✓Rate and budget controls
- ✓Audit trail of actions taken
Admin console
Govern the assistant fleet.
- ✓Model routing per feature
- ✓Cloud fallback policy controls
- ✓Feature flags and rollouts
- ✓Private telemetry dashboards
- ✓Quality and escalation reports
- ✓Permission template management
- ✓Cost monitoring for cloud usage
- ✓Compliance exports
Developer kit
Extend the platform safely.
- ✓Intent and tool SDK
- ✓Sandboxed action testing
- ✓Model evaluation harness
- ✓Prompt and persona templates
- ✓Versioned schema for actions
- ✓On-device test suite
- ✓Integration webhooks
- ✓Documentation and samples
The admin control plane: running the assistant fleet as an operator
Four consoles, one view of models, policy, and money.
Model console
Model routing per feature with cloud fallback policy controls, cost monitoring for cloud usage, and rollout safety through feature flags and staged rollouts.
Privacy console
Permission template management, private telemetry dashboards with no raw content, and the audit surface that makes every request hop enumerable.
Quality console
Quality and escalation reports from aggregated signals, model evaluation through the SDK harness, and intent catalog versioning with confirmation requirements.
Money console
Tier and budget configuration across subscriptions and cloud metering, revenue reporting, and compliance exports for enterprise buyers.
How an assistant request runs
From trigger to action, with privacy enforced at every hop.
- 01
Trigger
The user types, speaks, or an automation fires; the request arrives with device, permission, and feature context attached.
✓ step 01 complete - 02
Route
The routing layer scores the request against local capability: summarize, rewrite, and context Q&A stay on-device; heavier generation may escalate per policy.
✓ step 02 complete - 03
Gather private context
For context questions, the local index retrieves the relevant documents and messages under the permissions the user granted, and nothing else.
✓ step 03 complete - 04
Generate or act
The model produces a summary, a rewrite, or a structured intent; destructive intents require explicit user confirmation before executing.
✓ step 04 complete - 05
Execute and confirm
The action runs through the app or OS surface, the result is shown, and the interaction is logged in the local audit trail.
✓ step 05 complete - 06
Learn, privately
Aggregated quality signals flow back to your dashboards without raw content, informing model tuning and routing policy.
✓ step 06 complete
AI inside the assistant
Five model-driven systems working the same request stream. Each ships pre-tuned for on-device assistant workloads and sharpens as your data compounds.
On-device language core
A quantized model tuned for summarization, rewriting, and intent extraction within mobile memory and thermal budgets.
Reads
User request, local context snippets, feature configuration
Changes
Assistant features that work offline and keep context private
Intent extraction
Maps free-form requests onto your structured intent catalog with parameter filling and confirmation requirements on risky actions.
Reads
Request text, intent catalog, user permissions
Changes
Natural language that actually drives the product, not just chats
Context retrieval
Ranks the private index for relevance per request so answers cite the user's own material, scoped strictly by permissions.
Reads
Local index, permission scopes, recency and relevance signals
Changes
Answers grounded in the user's world instead of generic guesses
Routing and fallback governor
Decides per request whether local inference suffices, and governs which features may escalate to cloud under your policy.
Reads
Request complexity, device state, policy configuration, cost budgets
Changes
Privacy and cost controlled by policy, not by accident
Private quality telemetry
Aggregates acceptance rates, corrections, and escalation frequencies without raw content, so quality work starts from evidence.
Reads
Aggregated interaction signals, feedback flags
Changes
Model and routing decisions tuned from real usage, not anecdotes
platform concepts
Concepts that keep an On-device assistant honest
The first concept is the privacy boundary. On-device models handle the default path, the private index is encrypted at rest, and cloud escalation is a per-feature policy decision, not a silent default. Every hop a request can take is enumerable, which is what makes the privacy story demonstrable to a security reviewer rather than aspirational.
The second is intent economics. The intent catalog is the contract between language and product: each intent declares parameters, permissions, and confirmation requirements, and the model only proposes what the catalog defines. That structure is what stops an assistant from being a chatbot with opinions and makes it a control surface you can audit.
The third is the routing budget. Local inference is free but bounded; cloud inference is capable but priced and policy-laden. Routing that weighs complexity, device state, and budget per request is what keeps unit economics predictable as usage grows, and it is admin-configurable so finance and legal both get a dial.
How your AI assistant platform earns
Subscription tiers gating assistant depth: context size, cloud fallback allowances, and team features.
Enterprise licensing with private deployment, compliance exports, and support SLAs.
Usage metering for cloud-fallback minutes and premium model access where you enable them.
Marketplace potential: tool and action integrations sold to power users or partners.
Every tier and budget lives in the admin console, so you can reprice as model costs shift without a code release.
Feature groups that decide whether the assistant clears review
Four groups, each mapped to the part of the privacy-first promise it protects.
On-device core
Quantized language and speech models running locally within mobile memory and thermal budgets, so summarization, rewriting, and triage work offline and sensitive context never leaves the device.
Private context and intents
An encrypted local index over the user's documents and messages scoped strictly by granted permissions, plus an intent catalog declaring parameters, permissions, and confirmation requirements on risky actions.
Governed cloud fallback
Cloud escalation as a per-feature policy decision with budgets, not a silent default, with routing that weighs complexity, device state, and cost per request so unit economics stay predictable.
Telemetry without raw content
Private telemetry dashboards fed by aggregated quality signals, feature flags and rollouts, permission template management, and compliance exports, so the privacy story is demonstrable to a security reviewer.
insight
The market numbers behind the On-device assistant opportunity
On-device AI is a fast-moving frontier where platform capabilities change quarterly, and we make no inflated claims about market size. The durable signal is that privacy regulation and enterprise procurement both keep pushing assistant workloads toward local inference, and few teams can engineer that layer well.
The engineering wedge is the privacy boundary and the routing budget: an assistant whose every hop is enumerable clears security review, and routing that treats cloud inference as a priced, governed path keeps margin predictable as usage grows. The revenue table below quantifies the lines.
Revenue lines, typical ranges, and what sets each ceiling
Five monetization lines across the platform, quantified against published industry benchmarks. Every tier and budget is admin-configurable; the figures here are modelling inputs, not projections of your platform's earnings.
| Criteria | Revenue line | How it is charged | Typical range | What sets the ceiling |
|---|---|---|---|---|
| Subscription tiers | Assistant depth, context size, cloud allowances | Plans commonly $8 to $50+ per month | Usefulness. Tiers hold while the assistant saves real time | |
| Enterprise licensing | Private deployment, SLAs, compliance exports | Licenses commonly $10,000 to $250,000+ per year | Privacy trust. Contracts hold when the audit passes | |
| Cloud-fallback metering | Metered cloud minutes and premium models | Metering commonly $5 to $100+ per month per heavy user | Routing discipline. Metering earns while budgets stay predictable | |
| Tool marketplace | Action and tool integrations sold to partners | Shares commonly 20% to 50% of sale value | Ecosystem supply. Revenue scales while the SDK stays good | |
| White-label licensing | Branded deployments for device makers | Licenses commonly $10,000 to $100,000+ | OEM demand. Deals close while per-device config stays cheap |
Every build ships with
Standard inclusions on every engagement, almost-ready or custom. Nine things you never have to ask for.
100% source code, full IP
The complete unencrypted codebase transfers to you, with documentation and deployment scripts.
White-label branding
Your logo, colors, and store accounts across all four panels, plus branded user communications.
Store submission and launch
App store and Play Console submissions handled end to end, with production monitoring switched on.
Model integration
On-device model bundling plus cloud provider configuration, with routing and fallback policies wired and tested.
AI models Pre-tuned
Intent extraction, retrieval, and routing arrive configured for assistant workloads, not as raw APIs.
Multi-language support
Interface languages and multilingual model coverage configured for your markets during the launch window.
Scalable cloud infrastructure
Containerized services for the cloud path, policy-governed, with autoscaling and regional deployment.
Admin analytics and reports
Quality dashboards, escalation reports, and CSV exports so your team starts informed on day one.
Post-launch support and updates
Included support window plus product updates covering bug fixes, OS compatibility, and model version upgrades.
Technology inside the assistant
On-device inference is where most teams stall. We engineer it first.
On-device inference
Quantized models with hardware acceleration on modern mobile chipsets, memory and thermal budgets profiled per device tier, and offline operation for core features.
Private index and retrieval
Encrypted local vector and keyword retrieval over user-approved sources, with strict permission scoping and no network egress on the default path.
Intent and action framework
A versioned intent catalog with parameter validation, permission checks, and confirmation gates, so natural language maps onto auditable actions.
Infrastructure
Policy-governed cloud services with regional routing. For the chat-first pattern, see our ChatGPT-style assistant build.
Integrations
Cloud model providers, speech services, and enterprise identity systems ship as configured integrations, so the assistant plugs into the stack your buyers already run.
Security and compliance
Role-based access, local encryption, policy-governed egress, and audit logs come standard, so the privacy story survives due diligence.
Your launch timeline: live in 6 working days
The almost-ready AI build, day by day. Store review times can add a short buffer outside our control.
- 01
Days 1-2: scoping and branding
Feature set, privacy policy, and intent catalog locked with a written scope, and your identity applied across all four panels.
✓ step 01 complete - 02
Days 3-4: model and policy configuration
On-device models bundled and profiled on target devices, cloud fallback rules and budgets configured and tested.
✓ step 02 complete - 03
Days 5-6: context and action pilot
Private index wired to your approved data sources, intents exercised end to end, and quality telemetry verified against the privacy policy.
✓ step 03 complete - 04
Days 7-9: store review and go live
App store submissions with review buffers accounted for, production monitoring on, and full launch with post-launch support included.
✓ step 04 complete
Almost-ready AI launch or custom build: the honest comparison
Both tracks ship the same on-device core. The difference is time, price structure, and how much you want to change.
| Criteria | Almost-Ready AI Launch | Custom Build |
|---|---|---|
| Time to live | 6 working days from branding handoff | 3 to 4 weeks, scoped before work starts |
| Pricing | Fixed written quote after a short scoping call, no hourly meter | Estimated per module after the discovery call |
| Source code and IP | 100% ownership on delivery | 100% ownership on delivery |
| AI layer | Intent extraction, retrieval, and routing pre-tuned for assistants | Models and intents designed around your product surface |
| Privacy posture | On-device default with policy-governed fallback | Privacy architecture designed to your compliance regime |
| Device coverage | Current-generation iOS and Android tiers | Wider device matrices and embedded targets as scoped |
| Best for | Teams who want to ship this quarter | Products with bespoke surfaces or strict regimes |
$ moxace launch --nowSee the assistant before you commit.
Book a walkthrough and we will demo on-device summarization, intent actions, and the admin console, then send the written scope.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
What moves your quote
No two launches price the same. These are the factors that shape the written quote you receive after scoping.
Scope of customization
The almost-ready AI build covers the proven feature set. Custom intents, bespoke surfaces, or specialized models add scoped estimate work.
Device matrix
Model profiling and testing scale with the range of device tiers you support. Broad coverage means more configuration and test time, estimated up front.
Integrations
Cloud providers, speech services, and identity systems are wired during the build, and each integration is estimated individually.
AI tuning depth
Out of the box, models are pre-tuned for assistant workloads. Fine-tuning on your domain data is an optional, separately scoped workstream.
Add-on modules that deepen the platform
Extend the Apple Intelligence-style build once the core assistant loop is running.
Cross-device sync pack
Encrypted context sync across a user's devices with per-device policy, extending the private index beyond one handset.
Pro workflow automations
Chained intents that execute multi-step workflows with confirmation gates, turning the assistant into an automation surface.
Voice-first mode
Speech in, speech out, with on-device wake and transcription, opening hands-free and accessibility use cases.
Developer marketplace
A tool and action marketplace with sandboxed review, monetizing the SDK ecosystem you cultivate.
Enterprise compliance pack
Deployment controls, retention policies, and audit exports mapped to regulated buyers, sold as an annual add-on.
White-label device licensing
License the assistant layer to device makers under their own identity, with per-device model bundles and policy configuration.
/21
Why teams pick our apple Intelligence-style build
Assistant UX with privacy engineering that survives audit.
On-device first
Local inference, encrypted context, and policy-governed fallback are the architecture, not a marketing slide.
120+ AI engineers
A dedicated AI practice that has shipped 120+ AI products in production, applied to your roadmap from day one.
Full ownership
One hundred percent of the source code transfers to you. No per-request royalties on your own users, no black-box dependencies.
Adjacent products
Pair with our ChatGPT-style assistant for a cloud-first conversational product on the same engineering system.
Proof from platforms we have shipped
Canonical deployments from our own portfolio. Metrics are reported from live platforms, not projections.
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
Frequently asked questions
Security and integrity
Tap a card to see how the platform defends its privacy story.
The Default Path Stays Local
On-device models handle the default path and the private index is encrypted at rest, so core features work offline and sensitive context never leaves the device.
Cloud Escalation Is a Decision
Cloud fallback is a per-feature policy decision with budgets, not a silent default, so every hop a request can take is enumerable for a security reviewer.
Intents Are a Contract
The intent catalog declares parameters, permissions, and confirmation requirements, and the model only proposes what the catalog defines, which stops the assistant from improvising.
Destructive Actions Ask First
Risky intents require explicit user confirmation before executing, so an assistant with system access never becomes an unaccountable hand.
Telemetry Never Carries Content
Quality signals flow back to dashboards aggregated, without raw user content, so tuning improves the product without surveilling the user.
Budgets Keep Economics Honest
Routing weighs complexity, device state, and cloud budget per request, and every tier is admin-configurable, so unit economics stay predictable as usage grows.
Frequently asked questions
the process
What to expect when building with us
The engagement starts with a discovery call and ends with a written scope and quote before any work begins. On the almost-ready AI path you hand over branding assets and your intent shortlist, we profile models on your target devices, wire your data sources into the private index, and go live in 6 working days. Custom work is scoped as sprints and quoted in writing first, so there are no surprise invoices.
After launch you have post-launch support and product updates included, including model version upgrades as on-device capabilities evolve. Expect the first 90 days to be about intent quality: watching which requests fail, adding the intents your users actually want, and tightening routing policy as usage patterns emerge. The platform handles the privacy engineering; the intent catalog is where the assistant gets good.
Live in action
See It Live Before You Talk to Us
Open the web app, sign in with the printed demo credentials, or install the Android build — no signup, no sales call. What you see is what you launch.
$ moxace launch --nowShip a private AI assistant this month.
Book a free consultation and get a written scope and price.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
Independent Build
Moxace is an independent software development company. We are not affiliated with, sponsored by, or endorsed by Apple Intelligence.
Why This Name
The phrase “Apple Intelligence clone” is used descriptively. It is how the software industry refers to building a platform with similar functionality, and how buyers search for one.
Who Built This
The design and codebase are built entirely by our in-house team. Nothing is taken from the Apple Intelligence website, applications, or brand assets.
Trademarks
Apple Intelligence and all third-party names and marks remain the property of their respective owners, referenced here only to describe the category of software we build.
Read the full legal notice on our disclaimer page.