🧭 Introduction
In 2025, Apple revolutionized AI development on its platforms by introducing the Foundation Models framework. This API gives developers access to Apple’s private, on-device ~3B parameter language model that powers Siri and Apple Intelligence. Hushh.ai builds on top of this breakthrough to offer Personal Data Agents (PDAs)—intelligent, privacy-first assistants that run completely on-device.
This blog is a deep, hands-on walkthrough for iOS developers: how to set up your environment, initialize a PDA, build privacy-safe tools, and deploy an AI-powered app that aligns with Apple’s privacy-first ethos. If you’ve ever dreamed of building your own Siri—this is how to do it.
⚙️ Step 1: Setting Up Your Environment
To begin, ensure your stack is fully compatible:
- Xcode 15+ with Swift 5.9+
- macOS on Apple Silicon (M1 chip or higher)
- Deployment target: iOS 17 or later
- Use Apple’s built-in
FoundationModelsframework (no package needed)
Devices that lack Apple Neural Engine (e.g., older iPhones or Intel Macs) will not run the on-device model and will return .deviceNotEligible. Always check availability before showing the PDA UI:
if SystemLanguageModel.default.availability == .available {
// show PDA feature
} else {
// fallback or show alternate message
}To improve first-run latency, call session.prewarm() at app launch or before your PDA feature loads. This loads model weights into RAM and dramatically reduces startup time.
🧠 Step 2: Defining Your Agent with Guided Generation
The heart of the PDA is the LanguageModelSession object. You define your agent’s persona, rules, and behavior using Apple’s system instruction closure:
@State var session = LanguageModelSession {
"""
You are a helpful health coach. Use personal data tools to suggest wellness advice.
"""
}These instructions are never visible to the user but stay active throughout the session. The framework ensures all prompts pass through this system prompt, guarding against prompt injection.
🧱 Structured Output with @Generable
To get usable, Swift-native output, define types with the @Generable macro:
@Generable
struct HealthSummary {
let systolic: Int
let diastolic: Int
let advice: String
}You can now ask the model to populate this object:
let result: HealthSummary = try await session.respond(
to: userPrompt,
generating: HealthSummary.self
)This is Apple’s strongest innovation—typed output directly from AI, no fragile text parsing required.
🔧 Step 3: Building Tools — Real Data Access, Safe by Design
AI without data is a parrot. With tools, your PDA can securely fetch user data (e.g., from HealthKit) to generate meaningful output.
Define a Tool like this:
final class BloodPressureTool: Tool {
let name = "blood_pressure"
let description = "Fetch latest systolic and diastolic pressure from HealthKit."
@Generable struct Arguments {}
func call(arguments: Arguments) async throws -> ToolOutput {
let systolic = ... // HealthKit fetch
let diastolic = ...
let result = GeneratedContent(properties: [
"systolic": systolic,
"diastolic": diastolic
])
return ToolOutput(result)
}
}Then register tools at session creation:
session = LanguageModelSession(tools: [BloodPressureTool()]) { ... }Apple’s model was trained with tool-calling behavior, so it understands how to request tools based on instructions and user intent.
💡 Real Example in Action
When the user asks, “Check my last blood pressure and give advice,” the model:
- Identifies the need for real data.
- Calls
BloodPressureTool, receiving{systolic: 122, diastolic: 82} - Replies: “Based on your latest reading, your blood pressure is in the normal range. Keep maintaining a healthy diet and exercise.”
All of this happens offline, privately, and securely.
✅ Final Thoughts
Hushh’s Personal Data Agents empower users to own and use their data meaningfully. Apple’s Foundation Model makes the impossible—on-device, private LLMs—real. Together, they form the most powerful platform yet for building ethical, AI-native applications.
If you believe AI should serve the user, not surveil them, this is the stack for you.
