The mobile app development company for apps with AI built in

Our mobile app development services cover the whole path: product and UX design, the app itself, back-end APIs, testing and store submission. We plan offline mode and real-time sync where your users need them, wire up push notifications, and add analytics and crash reporting before launch, so you see how the app behaves in real hands instead of guessing.

Our iOS and Android app development runs as one plan, so features, release notes and store listings stay in step across both platforms. After launch we keep shipping fixes, new features and App Store Optimization, run by the same people who wrote the code.

• Product strategy, UX research and visual design
• Cross-platform or native iOS and Android builds
• Offline mode, real-time sync and push notifications
• Analytics, crash reporting and automated release pipelines
• App Store and Google Play submission and ASO

Most products are well served by one shared codebase. As a Flutter app development company, we reach for Flutter and Dart when you want a single codebase with a custom, consistent interface on iOS and Android. We use React Native when your team already works in React and TypeScript and wants to share logic with a web app.

Cross-platform is not always the answer. Apps that lean hard on device hardware, background processing or platform-specific interface patterns can justify native Swift and Kotlin. We recommend an approach during scoping, with the trade-offs in cost, speed and long-term maintenance written down, so you make the call on evidence rather than fashion.

As an AI app development company, we treat AI as a feature with a job to do, not a badge. Typical features include in-app assistants grounded in your own content, smart search, summaries, voice input with speech-to-text, document and photo capture, and personalized recommendations. Each one gets a clear purpose, a fallback when the model is unsure, and an easy way for users to correct it.

We decide where each model runs. Small on-device models can handle tasks like classification or transcription offline and keep data on the phone. Larger LLMs such as GPT, Claude or Gemini run behind your own API, where we add guardrails, caching and spend limits, and where you can switch providers later without shipping a new app version.

• Assistants that answer from your own content, with sources
• Voice input and transcription with Whisper or Deepgram
• Model calls behind your API, never keys in the app
• Usage limits and monitoring so AI costs stay predictable

Every build starts with a free 20-minute scoping call. From there we write a scope, split it into milestones such as design, a first testable build, beta and store launch, and agree a fixed price for each milestone before work starts. You see working software at every milestone, with weekly demos in between, so there are no surprises at the end.

If you want to prove one AI feature before committing to a full app, a fixed-scope, fixed-price AI pilot is a smaller first step. If you need steady capacity after launch, a dedicated AI pod on a monthly retainer keeps the same senior team on your roadmap.

We build and operate our own products, so we know what it takes to keep software working long after launch day. iQuanta's community-led learning platform, built with React, Node.js and Flutter, brings discussions, doubt solving, streaks and a notice board into one dashboard for a large community of aspirants.

Tapline is a multi-number WhatsApp team inbox with tickets, CRM deals and AI-assisted replies beside every chat. iSales, our AI-powered sales CRM, gives admissions teams role-based dashboards from counselor to CEO, with a role-aware AI copilot and masked calling. The same habits carry into every client app: real-time data, careful permissions and AI that helps people finish work.