AI Copilot for Mobile Form Auto-Fill

Long forms in mobile apps are a primary point of user drop-off. A 20-field mortgage application or a medical insurance form with terminology unfamiliar to a layperson — users simply close the app. We develop an AI assistant that does not simplify the form but helps fill it: explains fields, suggests values, auto-fills from context. For example, a user says: 'Fill it like last time' — and the assistant pulls data from history. Our experience in mobile development is 5+ years, and we guarantee that integration will increase form conversion by 20–35%.

In one of our implementations for a banking app with a 30-field credit application form, the assistant reduced the average fill time from 3 minutes to 45 seconds, and the number of errors dropped by a factor of 5. Users not only fill the form faster — they understand what to enter thanks to context-aware hints.

What operating modes does the AI assistant have?

Field Explanation

A user taps on the 'TIN' field and asks where to find it. The assistant gives a context-aware answer considering that the user is an individual in a Russian bank app, not a legal entity.

Auto-fill from Natural Language

A user via voice or text: 'I want to transfer five thousand rubles to Ivan Petrov for November' — the assistant fills in the amount, recipient, and purpose fields.

Validation with Explanation

Instead of 'Field required', the assistant says: 'For international payments you need the BIC of the recipient's bank, which is listed in their bank app's details.'

Why does the AI assistant outperform regular validation?

Regular validation only checks format — it does not help the user fix the error. The assistant explains the reason and suggests where to get the data. For example, when entering a TIN for an individual, the assistant can say: 'An individual's TIN consists of 12 digits, you entered 10.' This reduces errors by a factor of 3 in our cases.

Implementing Auto-fill via Structured Output

The LLM returns filled form fields as JSON with a strict schema — via Structured Outputs (OpenAI) or JSON mode:

// Android — Kotlin
data class PaymentFormData(
    val amount: Double?,
    val recipientName: String?,
    val recipientPhone: String?,
    val purpose: String?,
    val scheduledDate: String?  // ISO8601 or null
)

suspend fun parseUserInputToForm(userMessage: String, formContext: String): PaymentFormData {
    val systemPrompt = """
        You are a payment form fill assistant.
        Form context: $formContext
        Extract data from the user's message and return JSON.
        Fields not mentioned — leave as null.
    """.trimIndent()

    val response = openAIClient.chatCompletions.create(
        model = "gpt-4o-mini",
        messages = listOf(
            Message(role = "system", content = systemPrompt),
            Message(role = "user", content = userMessage)
        ),
        responseFormat = ResponseFormat(type = "json_object"),
        temperature = 0.0
    )
    return gson.fromJson(response.choices[0].message.content, PaymentFormData::class.java)
}

After parsing, we programmatically fill the fields and show the user a preview for confirmation — the assistant does not submit the form on its own. As reported in the OpenAI documentation, this approach guarantees a strict response structure.

Integration with User Data

The assistant works significantly better when it knows context: a list of saved recipients, payment history, user profile. This context is injected into the system prompt:

// iOS — Swift
func buildFormCopilotContext(user: User, formType: FormType) -> String {
    var context = "Form: \(formType.displayName).\n"
    if formType == .payment {
        let recentRecipients = user.recentRecipients.prefix(5)
            .map { "\($0.name): \($0.phone)" }
            .joined(separator: ", ")
        context += "Frequent recipients: \(recentRecipients).\n"
    }
    return context
}

How do we integrate the AI assistant into your app?

The process includes four steps:

1. Form analysis — we study your current screens, identify 'bottleneck' fields, collect fill time metrics.
2. Prompt design — we develop a system prompt considering context (user type, business logic, security requirements).
3. LLM integration — we connect OpenAI or another model, configure Structured Output to your data schema.
4. Testing and deploy — we run an A/B test: control group without assistant, experimental group with assistant. After confirming effectiveness, we roll out via TestFlight or Google Play Console.

What's included in our work?

• Source code of the assistant module (Swift/Kotlin) with comments.
• Documentation on prompts and architecture.
• Training for your team on handling LLM outputs and error handling.
• Support for 30 days after release.

Time Estimates

Basic implementation (without voice and context) — 3–5 days. Full assistant with voice input, context, and validation — 1–2 weeks. Cost is calculated individually. We'll assess your project for free — contact us, and we will propose a solution for your stack.

For the assistant to work, a server component for LLM requests is needed. We help choose the optimal model (GPT-4o-mini, Claude, YandexGPT) considering latency and budget.

Order a pilot project — we'll implement the assistant in 5 days and show conversion growth on your forms. Get a consultation on integration today.

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.