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iOS and Android apps with integrated AI features.

In-app chat assistants, personalized recommendations and image or voice recognition all change the cost and architecture decision depending on whether they run on-device or in the cloud. We make that call deliberately, based on latency and cost needs, then build and test the AI feature inside a native or cross-platform app.

5+
Platforms & tools mastered
+74%
Avg engagement/conversion lift
3
Core capability areas covered
14-day
Onboarding turnaround
What We Do

Amplipath AI Mobile App Development services

Your strategy is customized around your market, digital maturity, commercial goals and existing systems rather than being forced into a generic checklist.

01

Built around your aI Features

Rather than a generic starting point, we build aI Mobile App Development strategy directly from your aI Features needs — In-app chat assistants, Personalized recommendations, Image/voice recognition, Predictive text/smart suggestions — so the foundation is right from day one.

02

Specialist execution, real tools

We execute using Flutter, React Native, Core ML, TensorFlow Lite, structured around the platforms that actually matters for aI Mobile App Development: iOS (Swift), Android (Kotlin), Cross-platform (Flutter/React Native).

03

Transparent, KPI-tied reporting

Every deliverable — including aI feature scoping for the mobile context — is tracked against clear KPIs, with reporting that ties aI Mobile App Development activity directly back to revenue, not vanity metrics.

Deliverables

What can be included in your engagement

Final deliverables depend on your scope, but a typical engagement can combine the strategy, implementation and measurement components below.

✓
AI feature scoping for the mobile context
✓
An app build with integrated AI features
✓
An on-device vs. cloud AI decision framework applied
✓
App Store/Play Store submission support
SPECIFICATIONS & DELIVERABLES

What Is Included

AI Features
In-app chat assistantsPersonalized recommendationsImage/voice recognitionPredictive text/smart suggestions
Platforms
iOS (Swift)Android (Kotlin)Cross-platform (Flutter/React Native)
AI Integration Methods
On-device ML (Core ML/TensorFlow Lite)Cloud API calls (OpenAI/Claude/Gemini)
Thimin — AI Life Coach
●Voice AI
FEATURED WORK

Thimin — AI Life Coach

An AI-powered life coach built to support personal growth through natural voice conversations.

View Case Study→
Frequently Asked Questions

AI Mobile App Development FAQs

On-device processing can offer lower latency, stronger offline capability and greater control over certain data. Cloud processing supports more powerful models but requires connectivity and may introduce ongoing API costs, so some applications use a hybrid architecture.
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