# Developing Uber Clone App - Ai Gemini integrated

**URL:** <https://discuss.ai.google.dev/t/developing-uber-clone-app-ai-gemini-integrated/97250>\
**Category:** Gemini API\
**Tags:** gemini-15, api\
**Created:** [August 8, 2025, 5:23am UTC](https://discuss.ai.google.dev/t/developing-uber-clone-app-ai-gemini-integrated/97250 "2025-08-08T05:23:51Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Mohd\_asif](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/mohd_asif/32/32078_2.png) [@Mohd\_asif](https://discuss.ai.google.dev/u/Mohd_asif)\
**Post date:** [August 8, 2025, 5:23am UTC](https://discuss.ai.google.dev/t/developing-uber-clone-app-ai-gemini-integrated/97250/1 "2025-08-08T05:23:51Z")

</div>

Uber Clone App Development with AI Gemini Integration – Looking for Technical Guidance

Hey everyone,

I’m currently part of a team at Suffescom Solutions, where we’re working on building a feature-rich UBER CLONE APP with integrated AI capabilities using Gemini (via Vertex AI/Gemini API).

**We’re aiming to add intelligent features like:**

- Real-time chat support between drivers and riders

- Predictive ETA adjustments based on traffic/context

- Smart route optimization with real-time AI insights

- Context-aware voice interactions

**Our stack includes:**

- Frontend: Flutter

- Backend: Node.js with Firebase (Auth, Firestore)

- Maps: Google Maps SDK/API

- AI: Gemini API (experimental phase)

While we’ve made good progress on the base functionality, we’re running into a few roadblocks on the Gemini AI side:

1. Structuring prompts for real-time use (like contextual ride queries)

2. Handling Gemini’s streaming responses efficiently in the app

3. Aligning AI output with dynamic geo-location data

4. Cost-performance balance when scaling AI requests across multiple riders/drivers

We’re approaching this from both an R&D and product perspective — trying to make something both smart and scalable. I’d really appreciate it if anyone has:

- Experience integrating Gemini or similar LLMs into mobile apps

- Tips on using AI in real-time, latency-sensitive systems

- Open-source examples or best practices you’ve come across