BackendIM
An AI-powered backend development platform built at the very start of 2025. Users describe a backend in plain language, BackendIM generates the code, deploys it, and gives them a live API w/ test tools.
type
AI Product @ Engineering
Role
Lead AI & Infrastructure Engineer
platform(s)
Web
website
backend.im
BackendIM was built to remove the friction of configuring backend infrastructure, databases, and deployments for founders, frontend and mobile engineers who just want to ship.
We started building in early January 2025, before Claude Code launched and before agentic devtools became an industry. The AI code generation at the core of the product was an agentic pipeline: it received a prompt, planned and executed a sequence of file writes and operations, and streamed every action back to the client in real time.
As CTO, Lead AI and Lead Infrastructure Engineer, I oversaw the technical execution while working with a team of designers, product managers, frontend, backend and devops engineers. I contributed to the Next.js frontend, led major portions of the Python backend, and built the deployment orchestration engine almost entirely myself.
Architecture & AI Stack
The platform required a distributed system to handle AI code generation, Git version control, and real-time deployment feedback simultaneously.
The frontend was built with Next.js and TypeScript, integrating a Monaco editor backed by server-side Git operations so users can read, edit, commit, and push project files directly from the browser. The core API used Python, FastAPI, Celery, and Redis, handling authentication, Stripe billing, Git repository creation, environment variables, API testing, and the AI generation WebSocket.
For the AI layer, I setup and integrated LiteLLM Gateway, LangChain, and early Anthropic models to translate user prompts into working backend code. The generation loop streamed tool actions (file writes, etc) and summaries back to the client in real time via WebSockets.
Infrastructure
To safely execute and deploy AI-generated code in production, I built orcApp, a custom orchestration service written entirely in Go.
The orcApp exposed a WebSocket protocol the frontend connected to for namespace setup, deployments, test execution, log streaming, and database inspection. When a user requests a deployment, it provisioned an isolated Kubernetes namespace, set up a persistent volume, and injected environment variables securely via HashiCorp Vault. It then performed a zero-downtime deployment using Nginx and supervisor inside a container, health-checks the new process, switches the load balancer, and streamed every step back to the browser as it happened.
Market Impact
BackendIM launched right as Agentic AI devtools were starting to gain serious traction. By removing the entire DevOps layer from the builder's path, we gave indie founders and small teams something they actually needed.
The MVP delivered immediate results: 400+ projects created, 140+ early testers, considerable social traction and 40+ Daily Active Users from launch.
The Takeaway
The thing that validated BackendIM as a product was not the AI or the infrastructure. It was watching a founder with no backend experience deploy a working API in minutes without ever touching a terminal.
That is the actual job of a product engineer: make something complicated feel like nothing. Every decision around isolation, secrets management, and real-time deployment feedback was made with that user in mind, not the architecture.
Building this also reinforced something I carry into every project. Shipping fast on a weak foundation creates problems you pay for later, at the worst possible time. Getting the invisible parts right from the start is what lets a product grow without breaking.
If you are building something that sits at the intersection of AI and infrastructure and you want an engineer who has been in both trenches, reach out.
