I am Damian, leading the development of a startup focused on legal automation through Generative AI. We are currently based in Campinas, Brazil, and we are looking for a Senior Bubble Developer to lead the front-end development and complex API orchestrations.
Core Requirements:
Expert-level Bubble.io skills (Backend workflows, API Connector, Database optimization).
Project Scope: Building a scalable interface for legal professionals, focusing on speed, security, and seamless AI integration.
If you are interested, please send me your portfolio and a brief description of your experience with AI projects. Please start your response with the word “LOGIC” to ensure you’ve read the full post.
I’m very interested in the Senior Bubble Developer opportunity. I have strong experience building scalable web and mobile products, working with API integrations, automation workflows, and AI-powered solutions.
My background includes:
Advanced API integrations and complex workflow logic
AI-assisted development using OpenAI-based solutions and automation tools
Experience with JSON handling, webhooks, and third-party services
Front-end/product development focused on performance and user experience
Strong attention to secure architecture and data privacy
I’m currently working on AI-assisted coding projects and app development, including React Native and Expo applications, along with automation systems using n8n and AI agents.
I’ve already built AI-powered Bubble systems very similar to what you’re describing — including Claude/OpenAI integrations, JSON manipulation, webhook handling, and backend workflow orchestration.
One relevant project is Founder Stack, where users submit a raw startup idea and Claude generates a structured roadmap. I integrated Anthropic Claude, handled structured JSON responses, mapped outputs into Bubble database fields, and used backend workflows to organize and render the data.
I’m Moazzam, a Bubble.io developer with solid experience in backend workflows, API integrations, database optimization, and JSON manipulation ,A strong fit for what you’re building.
Here’s a quick overview of my relevant experience:
Expert-level Bubble.io — backend workflows, API Connector, database structuring LLM integrations — OpenAI, Anthropic & similar AI APIs JSON manipulation & Webhook orchestration Security-first approach — database privacy rules & access controls Building scalable, performance-focused interfaces
I’m excited about the legal automation space and would love to contribute to building a secure, seamless AI-powered platform for legal professionals.
I’ve reviewed your background, and the fact that you’re working with n8n and AI agents is particularly relevant for the app i m building. We aren’t looking for a basic CRUD app; we need a robust orchestration between Bubble and LLMs.
Since you mentioned automation systems, I’d like to know:
In your n8n/Bubble setups, how do you handle long-running AI tasks to avoid timeouts in the frontend?
What’s your experience with Vector Databases or RAG (Retrieval-Augmented Generation) within Bubble workflows?
Can you share a specific example of a complex JSON transformation you’ve handled between an AI output and a Bubble database?
If your technical approach aligns with our roadmap, I’d like to have a 20-minute deep dive this Wednesday afternoon.
Hi Damian, thanks for getting back to me. No worries about the forum reply issue.
You’re absolutely right — for the type of platform you’re building, the challenge is not CRUD, it’s orchestration, reliability, and scalability between Bubble, APIs, and LLM services.
In your n8n/Bubble setups, how do you handle long-running AI tasks to avoid frontend timeouts?
My preferred approach is to make all heavy AI processes asynchronous. Instead of waiting for the frontend request to complete, I trigger the task in the background and immediately return a “processing” state to the user.
A common structure would be:
Bubble creates a task/entity record with status = Pending
Bubble sends the request to n8n or backend workflow
n8n handles the AI generation / parsing / multi-step logic
Results are written back directly into the Bubble database through API workflows or Data API (POST/PUT/PATCH)
Bubble UI listens for status changes and updates automatically
This avoids frontend timeouts completely and creates a much better UX.
For larger jobs, I also use queue logic, retries, partial saves, and step logging so failures can be resumed instead of restarting the whole flow.
What’s your experience with Vector Databases or RAG within Bubble workflows?
I’ve worked with Supabase as both a relational backend and vector store for AI retrieval workflows.
Typical structure:
Upload files (PDFs, docs, contracts, legal files, etc.)
Extract and chunk content
Generate embeddings
Store vectors in Supabase
At prompt time, retrieve the most relevant chunks via similarity search
Inject context into OpenAI / Anthropic prompts
This is especially valuable for legal automation, document intelligence, internal knowledge bases, and reducing hallucinations.
Bubble works well here as the frontend/orchestration layer, while Supabase handles vector search efficiently.
Can you share a specific example of a complex JSON transformation between AI output and Bubble database?
Yes — I’m currently working on a system where I need to ingest complete PDF packages from a legacy platform that has no API access.
The process is:
Fetch uploaded PDF files from an external source
Use AI/OCR pipelines to extract structured data
Transform messy unstructured content into normalized JSON
Split nested objects into Bubble-compatible entities
Create relational records inside Bubble automatically
Example:
Raw PDF may contain:
customer info
contracts
addresses
invoices
dates in inconsistent formats
duplicated fields
missing values
I convert that into clean JSON like:
JSON
{
“customer”: {…},
“contracts”: […],
“documents”: […],
“billing”: {…}
}
Then n8n maps each node into Bubble tables with proper references, validations, and deduplication rules.
That kind of ETL + AI structuring is where I’m strongest: connecting systems that were never designed to integrate.
Final Note
From what you described, I believe the biggest value I could bring would be designing stable AI workflows inside Bubble rather than only building pages/UI.
If helpful, I’d also be glad to discuss how I’d architect a legal automation stack using Bubble + n8n + Supabase + LLMs.
Your project is a strong fit for my background because I have built several AI-driven workflow systems where the difficult part was not just the interface, but the orchestration behind it: structured data, API calls, JSON handling, document workflows, role-based access, and keeping the AI layer controlled enough for real business use.
I have direct experience integrating OpenAI into production-style systems, including:
Cococure AI WhatsApp Chatbot
I helped build an AI-powered WhatsApp automation system using OpenAI, LangChain, FastAPI, Redis, FAISS, and WATI. The work involved API orchestration, live business rules, conversation state, retrieval logic, and controlled response generation.
DocuMind.ai
I worked on a document intelligence platform using OpenAI embeddings, vector search, document ingestion, and RAG-style querying. That experience is directly relevant to legal automation where users may need to upload, search, summarize, classify, or generate documents from structured and unstructured content.
PrimeCareathome.com Compliance Platform
I built a secure, compliance-focused operations platform with role-based dashboards, document handling, OCR processing, and structured review workflows. While it was not legaltech, the privacy and records-management concerns are very similar: users need access only to the right data, workflows need to be traceable, and sensitive information has to be handled carefully.
For your Bubble build, I would focus heavily on:
Clean database structure for users, matters, documents, prompts, outputs, and review states
Proper Bubble Privacy Rules from the beginning
Reliable backend workflows instead of fragile page-level logic
Strong API Connector setup for OpenAI, Anthropic, or other LLMs
Careful JSON payload design and response parsing
Webhook handling for any external systems
Human review steps around AI-generated legal content
Performance optimization so the interface stays fast as usage grows
I would be interested in learning more about the current product stage, whether the Bubble app is already started, which LLM provider you plan to use, and what legal workflows you are automating first.
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