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.