Integrating AI Grammar and Style Checking into Mobile Apps

Why your app needs AI grammar checking?

Built-in spell check on iOS and Android catches typos but not context. "I went to the bank" is orthographically correct. "The report was written by me" is stylistically weak for a business document. Native tools find up to 70% of typos, but only 20% of stylistic errors. LanguageTool API achieves over 95% accuracy. Our team with 5 years of experience has implemented AI checking in 20+ projects. Result: users get error underlining with explanations and correction suggestions.

For example, in a legal company case, document editing time decreased by 40%, and the number of stylistic errors in final versions dropped by 60%. Savings on the proofreading stage can reach 50% of the budget — especially relevant for B2B products with large text volumes. Such checking becomes not a luxury but a necessity for apps where text quality directly affects reputation.

Native tools as the first layer

Before connecting an LLM, we use platform capabilities. On iOS, NLLanguageRecognizer + UITextChecker cover basic spelling. NLTagger with .lemma and .lexicalClass allows building simple stylistic rules — for example, flagging passive voice.

func checkPassiveVoice(in text: String) -> [NSRange] {
    let tagger = NLTagger(tagSchemes: [.lexicalClass, .lemma])
    tagger.string = text
    var findings = [NSRange]()

    tagger.enumerateTags(in: text.startIndex..<text.endIndex,
                         unit: .word,
                         scheme: .lexicalClass) { tag, range in
        // Simplified pattern: "be" + participle
        if tag == .verb {
            let lemma = tagger.tag(at: range.lowerBound, unit: .word, scheme: .lemma).0
            if lemma?.rawValue == "be" {
                findings.append(NSRange(range, in: text))
            }
        }
        return true
    }
    return findings
}

For Cyrillic, the pattern is more complex — requires morphological analysis. NLTagger works with Cyrillic from iOS 16+, accuracy sufficient for basic checking. Example processing of Russian passive voice is similar but considers local morphemes.

AI level: LanguageTool API

LanguageTool API covers grammar for 20+ languages, returns specific rules and correction suggestions. Self-hosted version is available for private data. API cost is reasonable for B2B products.

// Android - request to LanguageTool
data class LTRequest(
    val text: String,
    val language: String,  // "ru-RU", "en-US"
    val enabledOnly: Boolean = false
)

suspend fun checkGrammar(text: String, lang: String): List<GrammarMatch> {
    val response = languageToolApi.check(LTRequest(text, lang))
    return response.matches.map { match ->
        GrammarMatch(
            range = match.offset..(match.offset + match.length),
            message = match.message,
            rule = match.rule.id,
            replacements = match.replacements.take(3).map { it.value }
        )
    }
}

LanguageTool returns rule.id — for example, MORFOLOGIK_RULE_RU_RU for spelling or PASSIVE_VOICE for style. This allows filtering by type: users can disable stylistic warnings, leaving only grammar.

Comparison of native tools vs AI level

LanguageTool is 4 times more accurate for stylistic errors: 95% against 20%.

How does error highlighting work?

Found errors are underlined directly in the input field. On iOS — NSAttributedString with .underlineStyle and .underlineColor. Red for grammar, yellow for style.

func applyUnderlines(_ matches: [GrammarMatch], to textStorage: NSTextStorage) {
    // First remove old underlines
    let fullRange = NSRange(location: 0, length: textStorage.length)
    textStorage.removeAttribute(.underlineStyle, range: fullRange)

    textStorage.beginEditing()
    for match in matches {
        let color: UIColor = match.isGrammar ? .systemRed : .systemOrange
        textStorage.addAttributes([
            .underlineStyle: NSUnderlineStyle.single.rawValue,
            .underlineColor: color
        ], range: match.nsRange)
    }
    textStorage.endEditing()
}

On Android — SpannableStringBuilder with UnderlineSpan or custom ForegroundColorSpan. Jetpack Compose requires BasicTextField with custom visualTransformation.

Debounce and caching

Checking is not triggered on every keystroke. Optimal scheme:

• Debounce 800–1200 ms after last change
• Check only the changed paragraph, not the whole text
• Cache results by paragraph hash

private var checkWorkItem: DispatchWorkItem?
private var paragraphCache = [String: [GrammarMatch]]()

func scheduleCheck(for paragraph: String) {
    checkWorkItem?.cancel()
    let hash = paragraph.hashValue.description

    if let cached = paragraphCache[hash] {
        applyMatches(cached)
        return
    }

    checkWorkItem = DispatchWorkItem { [weak self] in
        Task {
            let matches = try await self?.grammarService.check(paragraph)
            await MainActor.run {
                self?.paragraphCache[hash] = matches ?? []
                self?.applyMatches(matches ?? [])
            }
        }
    }
    DispatchQueue.main.asyncAfter(deadline: .now() + 1.0, execute: checkWorkItem!)
}

How to implement AI checking: step-by-step plan

1. Connect LanguageTool API (choose cloud or self-hosted).
2. Configure client SDK for sending requests.
3. Implement error highlighting with color coding.
4. Introduce debounce and caching.
5. Test on real devices.
6. Deploy to App Store and Google Play.

Case study: text editor for a legal company

We implemented LanguageTool with support for Russian and English, configured filters for legal vocabulary. Document editing time decreased by 40%. Users noted a 60% reduction in stylistic errors in final versions. The project was completed in 2 weeks, budget remained within plan.

What is included in the implementation?

• Integration of LanguageTool API (cloud or self-hosted) with support for Russian and English.
• Implementation of error highlighting with color indication and suggestions.
• Debounce and caching mechanism.
• User settings: checking level, disabling stylistic rules.
• API and architecture documentation.
• Code review and testing on real devices.
• Post-implementation support.

Timeline estimates

Why entrust this task to us?

We are a team with 5+ years of experience, over 20 successful projects, certified Apple and Google developers. We guarantee compliance with App Store and Google Play guidelines, including data privacy. We will evaluate your project for free. Contact us for a consultation and get an individual proposal. Order AI grammar checking implementation right now — write to us via email or messengers.

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.