Android & Agentic AI App Development Services

A simple Android app typically takes 3-4 months from concept to launch. Mid-complexity apps with backend integration take 4-6 months. Enterprise-grade applications with complex integrations can take 6-12 months. Apps with AI features may add 2-4 weeks for model training and optimization, depending on complexity. We use agile sprints so you see working features every 2-3 weeks.

Native Android with Kotlin is best for maximum performance, platform-specific features like advanced camera or sensor access, on-device AI processing with Gemini Nano, or a polished Material Design experience. Cross-platform (Flutter, React Native) works well when you need both Android and iOS with shared business logic and faster time to market. Both approaches support AI integrations. We help you choose based on your requirements, budget, and timeline.

Simple apps: $25,000-$50,000. Mid-complexity with backend systems: $50,000-$150,000. Enterprise applications: $150,000+. Adding AI features like basic chatbots or image classification typically adds $20,000-$60,000, while advanced AI with custom-trained models can add more. We provide transparent estimates with tight daily budget control — no surprise invoices.

Yes. We specialize in adding AI to existing systems. Whether you need a chatbot, image recognition, predictive features, or on-device intelligence with Gemini Nano, we can enhance your current app without a full rebuild. We start with an assessment to identify which AI features deliver the most value for your specific app and users.

On-device AI (Gemini Nano, LiteRT, MediaPipe) runs on the user’s phone — faster, works offline, zero per-query cost, keeps sensitive data private. Cloud AI (Gemini Pro, Firebase AI) handles complex tasks, processes larger data, works across all devices. Most modern apps use a hybrid approach. We design the right architecture based on your requirements.

Yes. We offer ongoing maintenance: bug fixes, OS updates, performance optimization, feature enhancements, and Play Store management. For AI-powered apps, this includes model retraining with new data, AI performance monitoring, and deploying improved models. Many clients have been partners for 5+ years.

Kotlin (primary), Jetpack Compose, Android Studio with Gemini AI assistant, Retrofit/OkHttp, Room/Firebase, Hilt, Coroutines/Flow, Material Design 3. For AI: Gemini Nano, ML Kit GenAI APIs, LiteRT, MediaPipe, Firebase AI, custom TensorFlow/PyTorch models. Cross-platform: Flutter, React Native, Kotlin Multiplatform.

Automated unit/UI tests, manual QA across real devices and Android versions, security audits for encryption and API protection, performance profiling. For AI features: model accuracy validation, bias testing, edge case analysis. Our QA automation achieves 97% data detection accuracy.

Yes. Deep experience with CRMs (Salesforce), ERPs, payment gateways, IoT devices, cloud platforms (AWS, Azure, Google Cloud), and custom APIs. We also integrate AI services — ML platforms, data pipelines, and AI APIs. With 400+ projects, enterprise integration is a core strength.

No. AI-powered mobile apps generated nearly $1.3 billion in revenue in 2024 — a 180% YoY increase — and businesses of all sizes are adopting these features. Google’s ML Kit and GenAI APIs make it possible without massive budgets. On-device AI features have zero per-query costs. Our All-In AI program is specifically designed to make agentic AI accessible for small and midsize businesses.

Yes — this is a core strength. We build Android applications for industrial rugged tablets, Android Automotive in-vehicle systems, kiosks and POS terminals, medical devices, wearables (Wear OS), digital signage, embedded IoT controllers, and drones. Android (AOSP) is the most versatile platform for deploying autonomous AI across device types, and we have experience customizing AOSP builds for specialized hardware requirements.

Agentic AI refers to AI systems that don’t just respond to commands — they monitor, anticipate, decide, and act autonomously. On Android devices, this means AI agents that run perception-reasoning-action loops: a camera on a factory tablet detects a quality defect, the agent reasons about severity, and takes action (alerts the operator, logs the incident, adjusts the production line). These agents run on-device for real-time response or coordinate with cloud agents for complex decisions. Learn more about our agentic AI approach.