AI Pitfalls in Mobile Development: Common Mistakes That Kill App Performance and User Experience

The promise of artificial intelligence in mobile applications has captivated developers and business leaders alike, but the reality of implementation tells a sobering story. Recent industry analysis reveals that 85% of AI projects fail to deliver expected business value, with mobile implementations facing even steeper challenges due to device constraints and user experience expectations. The cost implications are staggering: organizations typically waste 60-80% of their AI development budget on features that either never ship or are quickly abandoned by users.

The mobile landscape presents unique complexities for AI implementation. Unlike server-side deployments where resources are abundant and controllable, mobile environments demand careful consideration of battery life, memory constraints, network limitations, and diverse hardware capabilities. When AI features are poorly implemented, they don't just underperform—they actively damage user experience through excessive battery drain, app crashes, privacy violations, and confusing interfaces that break user mental models.

This comprehensive analysis examines the most damaging patterns in mobile AI implementation, drawing from real-world failures across major applications. From recommendation engines that destroyed battery life to AI search features that drove away 60% of users, these cautionary tales provide essential guidance for technical decision-makers navigating the AI implementation landscape. The following framework will help you distinguish between AI applications that genuinely enhance user value and those that merely add complexity while destroying performance.

The Over-Engineering Trap: When AI Becomes a Solution Looking for a Problem

The most pervasive mistake in mobile AI development is implementing machine learning solutions for problems that traditional algorithms solve more effectively. This over-engineering trap typically emerges when development teams, pressured to incorporate AI features for competitive positioning, force intelligent solutions into scenarios where simple rule-based systems would provide superior performance and maintainability.

Consider user interface sorting and filtering operations. Many applications unnecessarily implement ML-based recommendation systems for basic content organization when deterministic algorithms would provide faster, more predictable results. A news application, for instance, might deploy a complex neural network to sort articles by relevance when a straightforward scoring algorithm based on recency, user interaction history, and category preferences would deliver superior performance with 90% less computational overhead.

Performance impact analysis reveals stark differences between over-engineered AI solutions and traditional approaches. ML models for simple classification tasks typically consume 15-30 times more CPU cycles than equivalent rule-based systems, while requiring constant memory allocation for tensor operations. This computational burden translates directly to reduced battery life and increased thermal throttling on mobile devices.

Resource consumption patterns of over-engineered solutions follow predictable trajectories. Initial model loading can consume 50-200MB of memory, with inference operations requiring additional temporary allocations that stress the garbage collector. Network requests to cloud-based AI services compound the problem by consuming cellular data and introducing latency that degrades user experience.

The decision matrix for AI versus conventional implementation should prioritize three key factors: problem complexity, data availability, and performance requirements. AI solutions become justifiable when dealing with unstructured data, complex pattern recognition, or scenarios where rule-based approaches would require hundreds of conditional statements. However, for straightforward classification, basic personalization, or deterministic workflows, traditional algorithms consistently outperform machine learning approaches in mobile environments.

Alternative approaches using conventional algorithms often provide surprising effectiveness. Time-series analysis for user behavior patterns, collaborative filtering for simple recommendations, and weighted scoring systems for content ranking can achieve 80-90% of the effectiveness of ML solutions while consuming a fraction of the resources. The remaining 10-20% improvement from AI rarely justifies the implementation and maintenance overhead in mobile contexts.

Privacy and Security Violations: The Trust-Killing Mistakes

Privacy violations in AI implementations represent some of the most damaging mistakes organizations can make, with consequences extending far beyond technical performance to legal liability and brand reputation destruction. The complexity of AI systems often obscures data collection practices, creating compliance gaps that expose organizations to regulatory action and user backlash.

Data collection beyond functional requirements represents the most common privacy violation pattern. AI implementations frequently vacuum up user data under the broad justification of "improving the user experience," collecting device identifiers, location history, contact lists, and behavioral patterns that exceed what's necessary for the stated functionality. A fitness application's AI coaching feature might collect detailed location tracks, heart rate patterns, and social connections when simple activity classification would suffice for the core functionality.

Inadequate anonymization and encryption practices compound privacy risks in AI implementations. Many mobile applications implement superficial anonymization techniques that can be easily reversed through correlation attacks or demographic inference. Hash-based pseudonymization, while appearing secure, often fails when combined with rich behavioral datasets that allow re-identification through pattern matching.

// Privacy-compliant data collection and anonymization for AI training
interface PrivacyCompliantDataCollector {
  private readonly ANONYMIZATION_SALT = process.env.ANONYMIZATION_SALT;
  private readonly dataRetentionDays = 90;
  
  async collectUserInteraction(userId: string, action: string, metadata: Record<string, any>): Promise<void> {
    try {
      // Apply differential privacy noise
      const noisyMetadata = this.addDifferentialPrivacyNoise(metadata);
      
      // Create anonymized user identifier
      const anonymizedId = await this.createAnonymizedId(userId);
      
      // Remove PII from metadata
      const sanitizedMetadata = this.removePII(noisyMetadata);
      
      // Implement data minimization
      const minimalData = this.applyDataMinimization(sanitizedMetadata, action);
      
      const dataPoint: TrainingDataPoint = {
        anonymizedUserId: anonymizedId,
        action,
        metadata: minimalData,
        timestamp: Date.now(),
        expiresAt: Date.now() + (this.dataRetentionDays * 24 * 60 * 60 * 1000)
      };
      
      await this.storeWithEncryption(dataPoint);
      
      // Log privacy compliance metrics
      this.recordPrivacyMetrics({
        dataType: action,
        anonymizationLevel: 'differential_privacy',
        retentionPeriod: this.dataRetentionDays
      });
      
    } catch (error) {
      this.handlePrivacyError(error, { userId, action });
      throw new PrivacyComplianceError('Failed to collect data in privacy-compliant manner', error);
    }
  }
  
  private async createAnonymizedId(userId: string): Promise<string> {
    const crypto = await import('crypto');
    return crypto.createHmac('sha256', this.ANONYMIZATION_SALT)
      .update(userId)
      .digest('hex')
      .substring(0, 16);
  }
  
  private addDifferentialPrivacyNoise(data: Record<string, any>): Record<string, any> {
    const epsilon = 0.1; // Privacy budget
    const noisyData = { ...data };
    
    // Add Laplace noise to numeric values
    Object.keys(noisyData).forEach(key => {
      if (typeof noisyData[key] === 'number') {
        const noise = this.generateLaplaceNoise(epsilon);
        noisyData[key] = Math.max(0, noisyData[key] + noise);
      }
    });
    
    return noisyData;
  }
  
  private removePII(data: Record<string, any>): Record<string, any> {
    const piiPatterns = [
      /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/, // Email
      /\b\d{3}-?\d{3}-?\d{4}\b/, // Phone
      /\b\d{4}\s?\d{4}\s?\d{4}\s?\d{4}\b/ // Credit card
    ];
    
    const sanitized = { ...data };
    
    Object.keys(sanitized).forEach(key => {
      if (typeof sanitized[key] === 'string') {
        piiPatterns.forEach(pattern => {
          sanitized[key] = sanitized[key].replace(pattern, '[REDACTED]');
        });
      }
    });
    
    return sanitized;
  }
}

Third-party AI service compliance failures create additional privacy risks that many organizations overlook. Cloud-based AI APIs often process user data in jurisdictions with different privacy regulations, creating compliance gaps. Major AI service providers may change their data handling practices, terminate GDPR compliance certifications, or experience data breaches that expose client information.

GDPR and CCPA violation patterns in AI implementations typically involve three common mistakes: lack of explicit consent for AI processing, failure to provide meaningful transparency about automated decision-making, and inability to honor deletion requests when user data has been incorporated into trained models. The "right to explanation" under GDPR becomes particularly challenging with complex neural networks that lack interpretability.

User consent fatigue represents a growing challenge as applications request increasingly broad permissions for AI features. Users frequently accept privacy policies without understanding the implications, creating legal risks when consent is later challenged. Effective privacy compliance requires progressive consent mechanisms that clearly explain specific AI use cases and their data requirements.

Performance Disasters: AI Features That Cripple User Experience

Performance issues represent the most immediate and visible consequences of poor AI implementation in mobile applications. Unlike backend services where resource constraints can be addressed through horizontal scaling, mobile devices operate within fixed limitations that make performance optimization critical for user retention and app store rankings.

Battery drain patterns from poorly optimized models follow predictable trajectories that can be catastrophic for user adoption. Continuous inference operations, inefficient model architectures, and excessive network requests create sustained CPU utilization that can reduce battery life by 40-60%. A major social media application learned this lesson when their recommendation engine update caused widespread user complaints about device heating and rapid battery depletion, leading to a 23% decrease in daily active users within two weeks.

Memory leak identification in ML pipeline implementations requires specialized monitoring because traditional leak detection tools often miss tensor allocation patterns. TensorFlow Lite and Core ML models can create subtle memory leaks through improper resource disposal, retained graph references, and accumulating inference contexts that gradually consume available RAM until the application crashes.

// Memory-efficient TensorFlow Lite model loading and inference management
class MemoryEfficientMLManager {
    private var interpreter: Interpreter? = null
    private var inputBuffer: ByteBuffer? = null
    private var outputBuffer: ByteBuffer? = null
    private val memoryTracker = MemoryUsageTracker()
    
    companion object {
        private const val MAX_MEMORY_THRESHOLD_MB = 150
        private const val MODEL_RELOAD_INTERVAL_HOURS = 6
    }
    
    @Synchronized
    fun loadModel(context: Context, modelPath: String): Result<Unit> {
        return try {
            memoryTracker.recordPreLoadMemory()
            
            // Configure interpreter options for memory efficiency
            val options = Interpreter.Options().apply {
                setNumThreads(2) // Limit thread usage
                setUseXNNPACK(true) // Enable optimized operations
                setAllowFp16PrecisionForFp32(true) // Reduce precision when safe
            }
            
            // Load model with memory mapping to avoid full loading
            val modelBuffer = loadModelBuffer(context, modelPath)
            interpreter?.close() // Ensure previous interpreter is cleaned up
            interpreter = Interpreter(modelBuffer, options)
            
            // Pre-allocate buffers to avoid repeated allocation
            allocateBuffers()
            
            memoryTracker.recordPostLoadMemory()
            
            // Verify memory usage is within acceptable limits
            if (memoryTracker.getCurrentMemoryUsageMB() > MAX_MEMORY_THRESHOLD_MB) {
                throw MemoryThresholdExceededException("Model loading exceeded memory threshold")
            }
            
            Result.success(Unit)
            
        } catch (exception: Exception) {
            cleanup()
            memoryTracker.recordLoadFailure(exception)
            Result.failure(MLLoadException("Failed to load ML model", exception))
        }
    }
    
    @Synchronized
    fun runInference(inputData: FloatArray): Result<FloatArray> {
        return try {
            val interpreter = this.interpreter 
                ?: return Result.failure(MLException("Model not loaded"))
            
            memoryTracker.recordInferenceStart()
            
            // Prepare input buffer
            inputBuffer?.rewind()
            inputData.forEach { inputBuffer?.putFloat(it) }
            
            // Run inference
            interpreter.run(inputBuffer, outputBuffer)
            
            // Extract results
            outputBuffer?.rewind()
            val results = FloatArray(getOutputSize())
            outputBuffer?.asFloatBuffer()?.get(results)
            
            memoryTracker.recordInferenceComplete()
            
            Result.success(results)
            
        } catch (exception: Exception) {
            memoryTracker.recordInferenceError(exception)
            Result.failure(MLInferenceException("Inference failed", exception))
        }
    }
    
    private fun allocateBuffers() {
        val inputShape = interpreter?.getInputTensor(0)?.shape()
            ?: throw MLException("Unable to determine input shape")
        val outputShape = interpreter?.getOutputTensor(0)?.shape()
            ?: throw MLException("Unable to determine output shape")
        
        val inputSize = inputShape.fold(1) { acc, dim -> acc * dim }
        val outputSize = outputShape.fold(1) { acc, dim -> acc * dim }
        
        inputBuffer = ByteBuffer.allocateDirect(inputSize * 4).apply {
            order(ByteOrder.nativeOrder())
        }
        
        outputBuffer = ByteBuffer.allocateDirect(outputSize * 4).apply {
            order(ByteOrder.nativeOrder())
        }
    }
    
    fun cleanup() {
        interpreter?.close()
        interpreter = null
        inputBuffer = null
        outputBuffer = null
        memoryTracker.recordCleanup()
    }
    
    fun getMemoryMetrics(): MemoryMetrics {
        return memoryTracker.getMetrics()
    }
}

class MemoryUsageTracker {
    private val runtime = Runtime.getRuntime()
    private var preLoadMemory: Long = 0
    private var postLoadMemory: Long = 0
    private var inferenceCount: Int = 0
    private var errorCount: Int = 0
    
    fun recordPreLoadMemory() {
        preLoadMemory = runtime.totalMemory() - runtime.freeMemory()
    }
    
    fun recordPostLoadMemory() {
        postLoadMemory = runtime.totalMemory() - runtime.freeMemory()
    }
    
    fun getCurrentMemoryUsageMB(): Long {
        return (runtime.totalMemory() - runtime.freeMemory()) / (1024 * 1024)
    }
    
    fun recordInferenceStart() {
        inferenceCount++
    }
    
    fun recordInferenceComplete() {
        // Log successful inference completion
    }
    
    fun recordInferenceError(exception: Exception) {
        errorCount++
    }
    
    fun recordLoadFailure(exception: Exception) {
        // Log load failure metrics
    }
    
    fun recordCleanup() {
        // Log cleanup metrics
    }
    
    fun getMetrics(): MemoryMetrics {
        return MemoryMetrics(
            modelLoadOverheadMB = (postLoadMemory - preLoadMemory) / (1024 * 1024),
            currentUsageMB = getCurrentMemoryUsageMB(),
            inferenceCount = inferenceCount,
            errorRate = if (inferenceCount > 0) errorCount.toFloat() / inferenceCount else 0f
        )
    }
}

Network bandwidth abuse through excessive API calls represents another common performance disaster. Applications that implement cloud-based AI services often make synchronous requests for every user interaction, creating network overhead that degrades responsiveness and consumes cellular data allowances. Batch processing, intelligent caching, and offline-first architectures can reduce network usage by 70-80% while improving perceived performance.

UI blocking operations during inference processing create jarring user experiences that immediately signal poor implementation quality. Synchronous model inference on the main thread causes interface freezes, dropped animations, and unresponsive controls that drive user abandonment. Proper implementation requires careful threading architecture with progress indicators and graceful degradation.

Cold start performance degradation analysis reveals that AI-enabled applications consistently show 2-3x slower launch times compared to equivalent traditional implementations. Model loading, initialization routines, and dependency injection for ML frameworks create startup overhead that particularly impacts user retention for new installs. Progressive loading strategies and lazy initialization can mitigate these issues while maintaining full functionality.

The Black Box Problem: Debugging and Maintenance Nightmares

The opacity of AI systems creates unprecedented challenges for mobile application debugging and maintenance. Unlike traditional software bugs that follow deterministic patterns, AI failures often manifest as subtle degradation in user experience that's difficult to detect, reproduce, and resolve.

Lack of explainability in critical user-facing decisions becomes particularly problematic in mobile applications where users expect immediate understanding of system behavior. When an AI-powered search feature returns unexpected results or a recommendation system suggests irrelevant content, users have no visibility into the decision-making process. This opacity creates support overhead and erodes user trust in application intelligence.

Debugging strategies for opaque AI system failures require fundamentally different approaches than traditional software debugging. Logging frameworks must capture model inputs, outputs, confidence scores, and intermediate states while respecting privacy requirements. Correlation analysis becomes essential for identifying patterns in failure modes that aren't immediately obvious from individual incident reports.

// Core ML model performance monitoring and battery usage optimization
import CoreML
import OSLog

class CoreMLPerformanceManager {
    private let logger = Logger(subsystem: "com.app.ml", category: "performance")
    private var model: MLModel?
    private let performanceMetrics = PerformanceMetrics()
    private let batteryMonitor = BatteryUsageMonitor()
    
    private enum ModelState {
        case unloaded
        case loading
        case ready
        case error(Error)
    }
    
    private var currentState: ModelState = .unloaded {
        didSet {
            logger.info("Model state changed to: \(String(describing: currentState))")
        }
    }
    
    func loadModel(from url: URL) async throws {
        currentState = .loading
        batteryMonitor.startMonitoring()
        
        do {
            let startTime = CFAbsoluteTimeGetCurrent()
            
            // Load model with configuration optimized for mobile
            let configuration = MLModelConfiguration()
            configuration.computeUnits = .cpuAndGPU
            configuration.allowLowPrecisionAccumulationOnGPU = true
            
            model = try await MLModel.load(contentsOf: url, configuration: configuration)
            
            let loadTime = CFAbsoluteTimeGetCurrent() - startTime
            performanceMetrics.recordModelLoad(duration: loadTime)
            
            currentState = .ready
            
            logger.info("Model loaded successfully in \(loadTime) seconds")
            
        } catch {
            currentState = .error(error)
            performanceMetrics.recordLoadError(error)
            batteryMonitor.stopMonitoring()
            throw MLPerformanceError.modelLoadFailed(underlying: error)
        }
    }
    
    func predict(input: MLFeatureProvider) async throws -> MLFeatureProvider {
        guard let model = model else {
            throw MLPerformanceError.modelNotLoaded
        }
        
        let predictionId = UUID().uuidString
        let startTime = CFAbsoluteTimeGetCurrent()
        
        do {
            // Monitor battery usage during inference
            let batteryLevelBefore = batteryMonitor.getCurrentBatteryLevel()
            
            let prediction = try await model.prediction(from: input)
            
            let inferenceTime = CFAbsoluteTimeGetCurrent() - startTime
            let batteryLevelAfter = batteryMonitor.getCurrentBatteryLevel()
            let batteryDrain = batteryLevelBefore - batteryLevelAfter
            
            // Record performance metrics
            performanceMetrics.recordInference(
                id: predictionId,
                duration: inferenceTime,
                batteryDrain: batteryDrain,
                inputSize: getInputSize(input),
                outputSize: getOutputSize(prediction)
            )
            
            // Log performance data for monitoring
            logger.info("""
                Prediction completed:
                ID: \(predictionId)
                Duration: \(inferenceTime)ms
                Battery drain: \(batteryDrain * 100)%
                Memory usage: \(getCurrentMemoryUsage())MB
                """)
            
            // Check performance thresholds
            if inferenceTime > 0.5 { // 500ms threshold
                logger.warning("Inference time exceeded threshold: \(inferenceTime)ms")
                performanceMetrics.recordPerformanceWarning(.slowInference(inferenceTime))
            }
            
            if batteryDrain > 0.001 { // 0.1% battery drain threshold
                logger.warning("High battery drain detected: \(batteryDrain * 100)%")
                performanceMetrics.recordPerformanceWarning(.highBatteryUsage(batteryDrain))
            }
            
            return prediction
            
        } catch {
            performanceMetrics.recordInferenceError(predictionId, error)
            logger.error("Prediction failed: \(error.localizedDescription)")
            throw MLPerformanceError.predictionFailed(id: predictionId, underlying: error)
        }
    }
    
    func getPerformanceReport() -> PerformanceReport {
        return PerformanceReport(
            modelLoadTime: performanceMetrics.averageLoadTime,
            averageInferenceTime: performanceMetrics.averageInferenceTime,
            totalInferences: performanceMetrics.inferenceCount,
            errorRate: performanceMetrics.errorRate,
            averageBatteryDrain: performanceMetrics.averageBatteryDrain,
            memoryUsage: getCurrentMemoryUsage(),
            thermalState: ProcessInfo.processInfo.thermalState.rawValue
        )
    }
    
    private func getCurrentMemoryUsage() -> Double {
        var info = mach_task_basic_info()
        var count = mach_msg_type_number_t(MemoryLayout<mach_task_basic_info>.size)/4
        
        let kerr: kern_return_t = withUnsafeMutablePointer(to: &info) {
            $0.withMemoryRebound(to: integer_t.self, capacity: 1) {
                task_info(mach_task_self_, task_flavor_t(MACH_TASK_BASIC_INFO), $0, &count)
            }
        }
        
        if kerr == KERN_SUCCESS {
            return Double(info.resident_size) / 1024.0 / 1024.0 // Convert to MB
        } else {
            return 0.0
        }
    }
    
    deinit {
        batteryMonitor.stopMonitoring()
        model = nil
    }
}

class BatteryUsageMonitor {
    private var isMonitoring = false
    
    func startMonitoring() {
        isMonitoring = true
        UIDevice.current.isBatteryMonitoringEnabled = true
    }
    
    func stopMonitoring() {
        isMonitoring = false
        UIDevice.current.isBatteryMonitoringEnabled = false
    }
    
    func getCurrentBatteryLevel() -> Float {
        return UIDevice.current.batteryLevel
    }
}

Version control challenges with model updates create unique maintenance burdens in AI-enabled mobile applications. Unlike traditional code updates that can be easily diffed and rolled back, model updates involve binary files that resist standard version control workflows. Model versioning requires specialized tooling and deployment strategies that many mobile development teams are unprepared to handle.

A/B testing complications with non-deterministic features compound the debugging challenge by introducing statistical variations that can mask real performance issues or create false positive alerts. Traditional A/B testing frameworks assume deterministic feature behavior, making them inadequate for AI features that might produce different outputs for identical inputs based on model state or random seeding.

Documentation requirements for AI-driven functionality must address both technical implementation details and business logic encoded in trained models. Standard API documentation fails to capture the nuanced behavior of AI systems, requiring specialized documentation that explains model capabilities, limitations, confidence thresholds, and expected failure modes.

Data Quality and Bias Issues That Destroy User Trust

Data quality problems in mobile AI implementations create compounding failures that often remain invisible until they cause significant user churn or regulatory scrutiny. Poor training data leads to biased recommendations, unfair user treatment, and systematic exclusion of user groups—issues that are particularly damaging in mobile applications where personalization drives engagement.

Training data quality assessment frameworks must account for the unique characteristics of mobile user behavior. Mobile usage patterns differ significantly from desktop or web analytics, with shorter session durations, context-dependent interactions, and device-specific constraints that affect data collection. Sampling biases emerge when training data over-represents certain user demographics, device types, or usage contexts.

Bias detection and mitigation in production systems requires continuous monitoring across multiple dimensions. Demographic bias analysis must examine recommendation fairness across age, gender, geographic, and socioeconomic lines. Temporal bias assessment identifies when model performance degrades for recent user interactions due to concept drift or seasonal variations in user behavior.

A dating application's AI matching algorithm demonstrated the devastating impact of unchecked bias when analysis revealed systematic preference amplification that perpetuated racial and socioeconomic discrimination. The algorithm learned to associate certain demographic characteristics with desirability, creating feedback loops that reinforced societal biases and ultimately led to regulatory investigation and substantial legal costs.

Edge case handling in real-world user scenarios becomes critical for maintaining user trust as AI systems encounter inputs outside their training distribution. Mobile applications face particularly diverse edge cases due to varying device capabilities, network conditions, and user contexts that may not be well-represented in training data.

Demographic representation analysis in recommendations reveals subtle but damaging bias patterns that destroy user trust. Content recommendation systems often exhibit popularity bias, demographic homogeneity, and temporal recency bias that create filter bubbles and reduce content diversity. Users quickly notice when recommendation systems fail to reflect their actual preferences or systematically exclude certain types of content.

Feedback loop contamination and model drift create long-term degradation in AI system performance that's often invisible until user engagement metrics show significant decline. Positive feedback loops amplify initial biases as user interactions with biased recommendations reinforce the underlying patterns. Negative feedback loops can occur when users learn to game recommendation systems, creating adversarial data that degrades model performance.

Integration Anti-Patterns: Common Architecture Mistakes

Architectural decisions in AI integration often create technical debt that becomes increasingly expensive to resolve as applications scale. Poor integration patterns not only impact immediate performance but also create maintenance burdens and scalability limitations that can cripple long-term product evolution.

Tight coupling between AI services and core app functionality represents one of the most damaging architectural anti-patterns. When AI features are deeply integrated into business logic, user interface components, and data flows, they become impossible to modify, replace, or remove without extensive refactoring. This tight coupling makes A/B testing difficult, increases deployment risks, and creates single points of failure that can crash entire application workflows.

Synchronous processing that blocks critical user flows destroys user experience and creates perception of poor application quality. Many implementations make the mistake of integrating AI inference directly into user interaction paths, creating mandatory delays that interrupt natural usage patterns. Search features that require AI processing before displaying results, form validation that depends on ML models, and navigation systems that wait for recommendation loading all violate user expectations for immediate responsiveness.

// Asynchronous AI processing with proper error handling and user feedback in Flutter
import 'dart:async';
import 'dart:isolate';
import 'package:flutter/material.dart';

class AsyncAIProcessor {
  static const Duration _defaultTimeout = Duration(seconds: 10);
  static const int _maxRetryAttempts = 3;
  
  final Map<String, Completer<AIResult>> _pendingRequests = {};
  final StreamController<AIProcessingStatus> _statusController = 
      StreamController<AIProcessingStatus>.broadcast();
  
  Isolate? _processingIsolate;
  SendPort? _isolateSendPort;
  
  Future<void> initialize() async {
    try {
      final receivePort = ReceivePort();
      
      _processingIsolate = await Isolate.spawn(
        _isolateEntryPoint,
        receivePort.sendPort,
      );
      
      final completer = Completer<SendPort>();
      
      receivePort.listen((message) {
        if (message is SendPort) {
          completer.complete(message);
        } else if (message is AIProcessingResult) {
          _handleProcessingResult(message);
        } else if (message is AIProcessingError) {
          _handleProcessingError(message);
        }
      });
      
      _isolateSendPort = await completer.future.timeout(_defaultTimeout);
      
    } catch (error) {
      throw AIInitializationException('Failed to initialize AI processor: $error');
    }
  }
  
  Future<AIResult> processAsync({
    required String requestId,
    required Map<String, dynamic> inputData,
    Duration? timeout,
    VoidCallback? onProgress,
  }) async {
    final effectiveTimeout = timeout ?? _defaultTimeout;
    final completer = Completer<AIResult>();
    
    _pendingRequests[requestId] = completer;
    
    try {
      // Validate input data
      _validateInputData(inputData);
      
      // Send processing request to isolate
      _isolateSendPort?.send(AIProcessingRequest(
        requestId: requestId,
        inputData: inputData,
        timestamp: DateTime.now(),
      ));
      
      // Notify UI about processing start
      _statusController.add(AIProcessingStatus(
        requestId: requestId,
        status: ProcessingState.started,
        progress: 0.0,
      ));
      
      // Set up timeout handling
      final timeoutTimer = Timer(effectiveTimeout, () {
        if (!completer.isCompleted) {
          _handleTimeout(requestId);
        }
      });
      
      // Set up progress updates
      final progressTimer = Timer.periodic(
        const Duration(milliseconds: 500),
        (timer) {
          if (completer.isCompleted) {
            timer.cancel();
          } else {
            _statusController.add(AIProcessingStatus(
              requestId: requestId,
              status: ProcessingState.processing,
              progress: _estimateProgress(requestId),
            ));
          }
        },
      );
      
      final result = await completer.future;
      
      timeoutTimer.cancel();
      progressTimer.cancel();
      
      _statusController.add(AIProcessingStatus(
        requestId: requestId,
        status: ProcessingState.completed,
        progress: 1.0,
        result: result,
      ));
      
      return result;
      
    } catch (error) {
      _pendingRequests.remove(requestId);
      
      _statusController.add(AIProcessingStatus(
        requestId: requestId,
        status: ProcessingState.error,
        error: error.toString(),
      ));
      
      rethrow;
    }
  }
  
  Stream<AIProcessingStatus> get statusStream => _statusController.stream;
  
  void cancelRequest(String requestId) {
    final completer = _pendingRequests.remove(requestId);
    if (completer != null && !completer.isCompleted) {
      _isolateSendPort?.send(AICancelRequest(requestId: requestId));
      completer.completeError(AICancellationException('Request cancelled by user'));
      
      _statusController.add(AIProcessingStatus(
        requestId: requestId,
        status: ProcessingState.cancelled,
      ));
    }
  }
  
  void _handleProcessingResult(AIProcessingResult result) {
    final completer = _pendingRequests.remove(result.requestId);
    if (completer != null && !completer.isCompleted) {
      completer.complete(result.aiResult);
    }
  }
  
  void _handleProcessingError(AIProcessingError error) {
    final completer = _pendingRequests.remove(error.requestId);
    if (completer != null && !completer.isCompleted) {
      completer.completeError(AIProcessingException(error.message));
    }
  }
  
  void _handleTimeout(String requestId) {
    final completer = _pendingRequests.remove(requestId);
    if (completer != null && !completer.isCompleted) {
      completer.completeError(AITimeoutException(
        'Processing timeout for request: $requestId'
      ));
    }
  }
  
  voi