Function Calling (Tool Use) for AI Assistant in Mobile Apps

We integrate Function Calling (Tool Use) into mobile apps — a mechanism where the AI model does not try to answer the question 'what's the weather tomorrow' on its own, but returns a structured JSON describing what needs to be called: {"name": "get_weather", "arguments": {"city": "Minsk", "date": "tomorrow"}}. The app executes the call, passes the result back, and the model generates the final answer. For OpenAI it's tools, for Anthropic — tool_use, for Google — function_calling. Our 5+ years of experience and 30+ successful projects ensure reliable integration. Request a consultation to evaluate your scenario.

Where it actually breaks on mobile

The most common problem is incorrectly described JSON Schema for tools. The model selects a tool based on the description and parameter schema. If the schema is vague ('pass what's needed'), the model either doesn't call the tool at all or passes parameters in the wrong type. Concrete case: the amount field described as string instead of number — the model passes "150", the deserializer expects Double, the app crashes with JsonDataCorruptedException. Gson and Moshi by default do not silently convert strings to numbers.

The second bottleneck is parallel tool calls. GPT-4 and Claude 3 can return multiple tool_calls in one response. If processed sequentially, the user waits. The right approach on Android is async/await via coroutines (async { } + awaitAll()), on iOS — async let or TaskGroup. And crucially: all results must be returned to the model in one messages[] step with role: "tool" for each call — OpenAI requires exactly that, otherwise 400 Invalid request.

The third problem is an infinite call loop. If the tool returns an error, the model sometimes tries to call it again with the same parameters. Limit the number of iterations (usually 5–10 is sufficient) and explicitly pass the error in the content of the tool response — this helps the model switch to another strategy.

How to avoid infinite call loops?

Set an iteration limit (e.g., 8) and pass the error in the content of the tool response. The model, upon receiving the error message, will change its strategy. Also useful to add a flag in the tool schema to prevent the model from calling it again without parameter changes.

What to do with parallel calls?

Use asynchronous execution. On Android — coroutines with async and awaitAll(), on iOS — TaskGroup. Collect all results in an array and send in one message with role: "tool". This reduces latency and meets API requirements. Comparison: properly implemented async execution reduces response time by 3 times compared to sequential processing.

ToolDispatcher Architecture

// Android — tool dispatcher
class ToolDispatcher {
    private val tools = mapOf<String, suspend (JsonObject) -> String>(
        "get_weather" to ::handleGetWeather,
        "search_flights" to ::handleSearchFlights,
        "book_hotel" to ::handleBookHotel
    )

    suspend fun dispatch(toolName: String, args: JsonObject): String {
        return tools[toolName]?.invoke(args)
            ?: """{"error": "unknown tool: $toolName"}"""
    }
}

Each handler returns a String (JSON string of the result). The model gets text, not an object — this is fundamental. No need to serialize complex structures; enough with a clear JSON containing key data.

Tool descriptions should be as specific as possible:

{
  "name": "search_products",
  "description": "Searches products in catalog by name or category. Use when the user asks about a specific product or wants to browse the assortment.",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {"type": "string", "description": "Search query in user's language"},
      "category": {"type": "string", "enum": ["electronics", "clothing", "food"]},
      "limit": {"type": "integer", "default": 10, "maximum": 50}
    },
    "required": ["query"]
  }
}

The description field influences whether the model calls the tool. 'Search' is a poor description. 'Searches products in catalog when the user names a specific product' — the model understands the context of use.

Dialog State Management on Client

Function Calling requires storing the full message history: user → assistant (with tool_calls) → tool (result) → assistant (final answer). On mobile this means a proper data model for Message:

// iOS
enum MessageRole { case user, assistant, tool }
struct Message: Codable {
    let role: MessageRole
    let content: String?
    let toolCalls: [ToolCall]?   // only for role == .assistant
    let toolCallId: String?      // only for role == .tool
    let name: String?            // tool name for role == .tool
}

Save the entire chain in @State / ViewModel. If you trim history to save tokens, only cut early user/assistant pairs, but never cut an incomplete tool call cycle — the model will get a context error.

Provider Comparison for Function Calling

All three providers use JSON Schema ( Wikipedia: JSON Schema ) to describe parameters. Differences are in field names and response format, but the dispatcher architecture is universal.

What's Included in the Work

Integration of Function Calling for 3–5 tools — 2–3 weeks. With extended logic, parallel calls, and complex state management — 4–6 weeks. Request a consultation for an accurate estimate for your project. Get a working prototype within 10 days.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.