Do you want to add facial recognition, a voice assistant, or smart recommendations to your app, but don't know where to start? ML models require server infrastructure, optimization for mobile processors, and a well-designed data pipeline. We integrate AI/ML into mobile products: on-device inference for instant response and cloud models for complex tasks.
Developing an AI/ML mobile app means creating a product that learns from user data and becomes smarter with every interaction. Neural networks, computer vision, NLP, and recommendation systems — working ML solutions for iOS and Android that we integrate without performance loss.
On-device neural networks — TensorFlow Lite, Core ML, ML Kit. Models run on the phone without sending data to the server, providing instant offline response.
Computer Vision (CV) — object, face, text, and document recognition. The camera becomes an input interface for scanners and AR masks.
Natural Language Processing (NLP) — LLM-based chatbots, voice assistants, sentiment analysis, and text summarization.
Recommendation systems — personalized feeds and product recommendations based on user behavior.
iOS · Android · AI · ML
Machine learning unlocks new interaction scenarios. We integrate AI so the user feels the magic, not the lag.
Object, face, text (OCR), and barcode recognition. ML Kit, Core ML and TensorFlow Lite for on-device inference — the camera works as an offline scanner.
LLM-based chatbots, sentiment analysis, semantic search. We integrate OpenAI API, Google NLU, and custom fine-tuned models.
Content personalization based on user behavior. Hybrid models of collaborative and content filtering increase conversion by 20–40%.
Models on the device chip — Neural Engine in iPhone, NPU in Android. Quantization fits a neural network into 5–10 MB without loss of accuracy.
AI in a mobile app is not just a trendy feature, but a competitive advantage. Users get used to personalization and don't return to apps that don't adapt to them.
Integrating ML into a mobile app is a complex engineering task. We choose the optimal architecture: on-device for speed, cloud-based for complexity.
On-device ML (TensorFlow Lite, Core ML) — models on the phone without sending data to the server. Instant response and complete privacy.
Optimization for mobile processors — quantization FP16→INT8, compression to 5–10 MB, 30+ FPS on previous generation devices.
A/B testing of models — running multiple versions of ML models in parallel with comparison of accuracy and latency.
Cloud ML services — OpenAI API, Google Cloud ML, AWS SageMaker. Hybrid architecture: on-device for speed, cloud for depth of analysis.
ML infrastructure — data pipelines from collection to drift monitoring. MLOps with automatic retraining and versioning.
Data collection and labeling — pipeline for collecting training data from the app with anonymization and preparation for training.
AI models need a powerful backend. We develop the server part with ML inference support and connect REST/GraphQL API for the model to communicate with the app. Ready infrastructure for AI at any scale.
Ordering an AI app means getting a product where ML is not a decorative feature, but a key driver of value for the user.
Data pipelines, model training and deployment. MLOps with metric monitoring and automatic retraining.
Running multiple versions of ML models in parallel with comparison of accuracy and business metrics.
Quantization to INT8, pruning, hardware acceleration via Neural Engine and NPU. No overheating or battery drain.
AI in a mobile app is not a black box. It is an engineering system that must be fast, accurate, and predictable. We build exactly such systems.
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