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REST API Integration Layer for Automation and Product Workflows
Built a backend integration layer designed to reduce repetitive setup work and make service connections, automation, and product development easier to ship.
This project shows my ability to support AI and product workflows with cleaner backend logic and integration structure.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Beyond'Norme is an AI-native execution OS replacing Kajabi, Circle, and Skool. Built solo. 99 API routes, 81 migrations, live payments, AI copilot, KPI engine — all production.
You'd be the founding technical hire. Day one: wire up the AI Insight Engine. Then 15-phase roadmap covering dashboard, Stripe, performance, AI, and Playwright.
Stack: Next.js 16, Supabase SSR, TypeScript 5.9, Groq/OpenAI/Anthropic, Vercel Edge.
Equity: 4-7% (4-yr vest, 6-mo cliff). Work begins when full team is assembled.
DM with a take on the platform.
amazing opportunity. if you need a design i would love to contribute .
𝐄𝐧𝐭𝐞𝐫𝐚 - 𝐑𝐞𝐬𝐢𝐝𝐞𝐧𝐭𝐢𝐚𝐥 𝐑𝐞𝐚𝐥 𝐄𝐬𝐭𝐚𝐭𝐞 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 (𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐩𝐫𝐨𝐩𝐞𝐫𝐭𝐲 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞)
◾ Frontend: React, Next.js, TypeScript, Tailwind CSS, Recharts, Mapbox GL JS
◾ Backend: Python, FastAPI, Node.js, PostgreSQL, Redis, Celery, REST & GraphQL APIs, OpenAI API, custom ML pipelines
◾ Infra: AWS (EC2, RDS, S3, Lambda), Docker, Kubernetes, GitHub Actions CI/CD, Elasticsearch
Entera is a platform built for institutional investors to discover, underwrite, and acquire single-family homes at scale - think hundreds of offers a day across multiple markets. I worked on the AI-driven deal analysis engine that pulls in MLS data, rental comps, and neighborhood signals to surface investment-grade properties ranked by projected yield and risk.
This is something interesting!
I developed an MVP of a RAG application for querying PDF documents.
The flow is simple: the user uploads a PDF, the system extracts and splits the text into chunks, generates embeddings, stores them in Chroma, and when a question is asked, retrieves the most relevant information so the AI can respond with context.
For this project, I used React, Node.js, Express, LangChain, Gemini, and Chroma Cloud.
The main goal was to better understand concepts such as chunking, embeddings, vector search, and the integration of AI models with vector databases. This is a first version, but it serves as a foundation for more advanced RAG applications in the future.
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