Overall Development Approach
Use No-Code/Low-Code Builders
Bubble.io or Bolt.io (primary web app builder)
Uizard.io, Figma, or Loveable for quick interface design mockups and prototyping
These can help you rapidly iterate UI/UX, then export or replicate designs in your chosen no-code platform.
OpenAI APIs for text-related AI features (talk-to-text, compliance checks, etc.).
Image Recognition API (e.g., Google Cloud Vision, Clarifai, or Roboflow with a pre-trained model) for basic automated measurement.
Fast Iteration
Emphasize speed over perfection.
Each module (referral intake, AI compliance checks, image measurement) can have a minimal feature set but still be demonstrable and testable.
2. Key AI/ML Tools to Leverage
2.1 AI Documentation/Compliance (Text-Based)
OpenAI’s GPT-3.5 or GPT-4
Use case: Validate text fields (e.g., “Are we missing any key details?”).
Implementation: Send form data to GPT via API, prompt it to identify what’s missing or if further info is needed.
Bubble.io Integration: Use Bubble’s API Connector to pass text data to OpenAI and retrieve suggestions in real time.
Speech-to-Text
AssemblyAI, Rev.ai, or Google Cloud Speech-to-Text
Implementation: Record audio directly in the no-code front-end (Bubble or Bolt) and send to your chosen speech-to-text API. GPT can further refine or format the output.
2.2 Automated Image-Based Measurement
Google Cloud Vision or Clarifai
Use case: Identify object boundaries (e.g., “Measure the width/height of object X in the image”).
Implementation:
Upload image from Bubble’s front-end to a storage bucket (e.g., AWS S3).
Send the image URL to the Vision API, parse the result for bounding box coordinates.
Calculate approximate measurements or keep it simple: highlight area/boundaries.
Return the result to Bubble/Bolt for display.
Custom Model via Roboflow
If you need a specialized model, you could quickly train a small dataset of labeled images (e.g., bounding boxes).
Connect Roboflow’s hosted inference endpoint to your no-code app with an API call.
2.3 Proximity/Route Optimization
Google Maps Platform
Use case: Distance matrix or geocoding to find the closest available resource (e.g., location-based assignment).
Implementation:
Capture user/patient addresses.
Use Google Maps Distance Matrix or Geocode API to compute distances.
Return the sorted list to your front-end, auto-assign the top match.
3. Breakdown of Two-Week MVP Timeline
Below is a day-by-day or phase-by-phase breakdown to help keep the project on track.
Phase 1 (Days 1–3): Set Up & Basic UI
UI Design - (I have started rough draft)
Create low-fidelity mockups in Uizard.io or Figma for 3–4 core screens:
Home/Dashboard, Data Entry/Intake Form, AI Compliance Screen, Image Upload & Results.
Bubble/Bolt Project Setup
Create a new app, set up user roles, basic pages, and initial navigation. (I have started this as well)
Add necessary plugins or API Connectors (OpenAI, Google Maps, etc.).
Core Data Model (I have database 95% Complete)
In Bubble, define the database structure (e.g., “Requests,” “Users,” “Images,” “Measurements,” “Notes”).
Keep it minimal: Name, Location, Status, Notes, etc.
Phase 2 (Days 4–6): AI Integration
Text Compliance & GPT
Set up an API workflow in Bubble/Bolt to send text from your input fields to OpenAI.
Build a simple prompt: “You are an AI assistant checking if any critical details are missing in the following description: [User’s text]. List required details if missing, or respond ‘Looks good’ if complete.”
Display suggestions in your UI in real-time or upon form submission.
Speech-to-Text
Integrate a solution like AssemblyAI or Google Cloud Speech:
Provide a record button in the Bubble front-end.
On stop, upload the audio file to the speech API.
Return transcription and pass it to the GPT step for final check.
MVP tip: If speech-to-text is too time-consuming, store audio recordings and add transcription “asynchronous” (like a button “Get Transcript” that calls the API).
Basic Testing
Create 2–3 test entries to ensure GPT prompts work and returned suggestions are displayed properly.
Phase 3 (Days 7–9): Image Processing & Measurement
Image Upload Flow
In Bubble, create an upload form (File Uploader).
On submission, store the image in a temp folder (Bubble’s built-in storage or your own S3 bucket).
Vision API Integration
Use the API Connector to pass the image URL to Google Cloud Vision (or Clarifai).
For MVP, let the system just detect edges or labels. If measurement is complex, you can approximate with bounding box or request custom annotation.
Return the data (bounding box coordinates or textual analysis) to your Bubble workflow.
Calculate approximate dimension if relevant or simply display “Measurement = X cm.”
Front-End Display
Show the user the original image plus the measurement output.
Save results to your data type (e.g., “Measurement Record”: user, image URL, dimension results).
Basic Testing
Test with sample images to confirm the flow (upload → analyze → return results → display).
Phase 4 (Days 10–12): Proximity Logic & Routing
Set Up Location Fields
In your database, store addresses or lat/long for each user/resource.
Google Maps Integration
Geocode the user’s address to retrieve lat/long and store it.
For matching, use the Distance Matrix API to compare the new request location with potential resources.
Auto-Assignment
Create a workflow: on request submission, run a small “Find Nearest Resource” function.
Return the top match (lowest distance) and auto-assign.
Test
Enter a sample address, ensure the system picks the resource with the smallest distance.
Phase 5 (Days 13–14): Polish, Final Testing & Demo
UI/UX Polish
Adjust design, ensure forms are user-friendly.
Clean up navigational flows—so it’s easy to jump between screens.
Testing & QA
Gather a small test group or internal team to run through major workflows.
Fix any showstoppers or major bugs.
Demo Preparation
Create a simple script that demonstrates:
Submitting a request with voice-to-text notes.
AI compliance check in real time.
Image upload for measurement.
Automatic assignment based on location.
Deployment
Use Bubble’s or Bolt’s built-in deployment (live environment).
Send test link or credentials to stakeholders for immediate feedback.
4. Must-Have vs. Nice-to-Have (MVP Scope)
Must-Have
Basic user signup & roles
Intake form with AI compliance prompts
Image upload + minimal bounding box measurement
Speech-to-text (or at least a simplified version if time is tight)
Proximity-based auto-assign
Nice-to-Have
Detailed analytics dashboards
Real-time chat or notification center
Complex UI animations or advanced design elements
Full HIPAA-like compliance & encryption (depending on your domain—this might be crucial later)
5. Suggested Tool Stack Overview
Design/Prototyping
Uizard.io, Figma, Loveable: Rapid wireframing and clickable prototypes.
Primary Builder
Bubble.io or Bolt.io: Main no-code environment for front-end, workflows, and database.
AI Services
OpenAI GPT-3.5/4: Real-time text checks (missing info, compliance suggestions).
Google Cloud Speech or AssemblyAI: Speech-to-text transcription.
Google Cloud Vision or Clarifai: Image analysis for measurement or bounding box detection.
APIs & Connectors
Google Maps (Distance Matrix, Geocoding) for proximity assignment.
Bubble API Connector or Integromat/Make for bridging external data easily if needed.
Hosting / Live
Host inside Bubble’s native hosting or push your no-code build to a custom domain.
If using external AI calls, ensure your usage and billing are set up properly (OpenAI, Cloud Vision, etc.).
6. Additional Tips for a Successful MVP in Two Weeks
Limit Scope
Keep each feature minimal (e.g., a single text field for compliance checks). Expand later.
Embrace Imperfections
The goal is functional, not perfect. Gather feedback, iterate.
Automate or Outsource
If building the image measurement logic is time-consuming, consider existing pre-trained models or a simplified approach that just returns bounding boxes.
Frequent Testing
Don’t wait until day 14. Test each new piece as soon as you implement it.
Document as You Go
Even minimal developer notes ensure that whoever joins or reviews the project can quickly get up to speed.