AI Vehicle Visualizer Integration - 13/11/2025 14:04 EST
Budget: $1,500 – $3,000 USD
Project: AI Photo-Based Vehicle Visualizer (Phase 2 of Existing FlutterFlow App)
I’m preparing the second phase of my mobile app and need a specialist who can build an AI-powered, photo-based vehicle visualizer that plugs directly into my current FlutterFlow project. The goal: a user uploads or snaps a photo of their car, selects a service, and instantly sees a realistic, high-quality before/after transformation.
Core Preview Engine Requirements
The visualizer must accurately simulate the following services:
• Window Tinting (realistic darkening, shadows, and glass reflections)
• Ceramic Coating Gloss/Depth
• Wax/Sealant Shine
• Exterior Cleaning/Detailing Enhancement
• Interior Deep Cleaning
(Tint needs to support selectable shades + selectable areas.)
The module must integrate seamlessly inside FlutterFlow using either:
• A custom Dart widget you build, or
• A secure, high-performance API endpoint I can call from FlutterFlow actions.
The output image should maintain factory-correct proportions, paint color accuracy, body reflections, panel geometry, and overall realism. The user experience must include either:
• A draggable Before/After slider, or
• A side-by-side comparison view.
Technical Expectations
I’m open to the model running via:
• On-device inference (TensorFlow Lite), or
• Cloud inference using Stable Diffusion, ControlNet, or another modern vision model.
Whichever route you choose, you need to outline:
• Hosting requirements
• Fine-tuning strategy (if needed)
• Latency expectations (target: under 10 seconds end-to-end on 4G)
• A path for scaling usage as the app grows
Deliverables
1. Complete source code for the AI workflow (training notebook, inference code, or API).
2. A FlutterFlow-ready widget or a clean integration guide with all required setup steps.
3. Documentation covering:
• Model limitations
• Expected accuracy
• Maintenance + retraining recommendations
4. A short demo video or GIF showing the entire flow:
Photo Upload → AI Processing → Before/After Preview.
Acceptance Criteria
• Each service produces a distinct and believable visual transformation.
• Output quality is consistent across different vehicle angles, lighting, and colors.
• Response time stays under 10 seconds.
• Codebase is clean, commented, and free of hidden dependencies.
What I Need From You
If this fits your skill set, please share:
• Examples of similar computer vision/image-to-image projects you’ve delivered
• Your preferred tech stack
• Your recommended approach for this build
• Any questions you need answered before starting
I’m preparing the second phase of my mobile app and need a specialist who can build an AI-powered, photo-based vehicle visualizer that plugs directly into my current FlutterFlow project. The goal: a user uploads or snaps a photo of their car, selects a service, and instantly sees a realistic, high-quality before/after transformation.
Core Preview Engine Requirements
The visualizer must accurately simulate the following services:
• Window Tinting (realistic darkening, shadows, and glass reflections)
• Ceramic Coating Gloss/Depth
• Wax/Sealant Shine
• Exterior Cleaning/Detailing Enhancement
• Interior Deep Cleaning
(Tint needs to support selectable shades + selectable areas.)
The module must integrate seamlessly inside FlutterFlow using either:
• A custom Dart widget you build, or
• A secure, high-performance API endpoint I can call from FlutterFlow actions.
The output image should maintain factory-correct proportions, paint color accuracy, body reflections, panel geometry, and overall realism. The user experience must include either:
• A draggable Before/After slider, or
• A side-by-side comparison view.
Technical Expectations
I’m open to the model running via:
• On-device inference (TensorFlow Lite), or
• Cloud inference using Stable Diffusion, ControlNet, or another modern vision model.
Whichever route you choose, you need to outline:
• Hosting requirements
• Fine-tuning strategy (if needed)
• Latency expectations (target: under 10 seconds end-to-end on 4G)
• A path for scaling usage as the app grows
Deliverables
1. Complete source code for the AI workflow (training notebook, inference code, or API).
2. A FlutterFlow-ready widget or a clean integration guide with all required setup steps.
3. Documentation covering:
• Model limitations
• Expected accuracy
• Maintenance + retraining recommendations
4. A short demo video or GIF showing the entire flow:
Photo Upload → AI Processing → Before/After Preview.
Acceptance Criteria
• Each service produces a distinct and believable visual transformation.
• Output quality is consistent across different vehicle angles, lighting, and colors.
• Response time stays under 10 seconds.
• Codebase is clean, commented, and free of hidden dependencies.
What I Need From You
If this fits your skill set, please share:
• Examples of similar computer vision/image-to-image projects you’ve delivered
• Your preferred tech stack
• Your recommended approach for this build
• Any questions you need answered before starting