Make Real
Make Real
Mahbub Rahman
Mahbub Rahman
Available for new projects

Build a Production-Ready AI SaaS MVP

Move beyond fragile API wrappers.

View My Work

EXECUTIVE SUMMARY

Mahbub Rahman is a senior full-stack AI engineer helping US founders build production-ready AI SaaS products with robust authentication, billing, and error-handled LLM integrations.

The Technical Reality

Building a demo with the OpenAI API takes an hour; building a resilient AI SaaS takes weeks. I avoid heavy, opinionated frameworks like LangChain for core logic, preferring the Vercel AI SDK or direct API calls. This ensures full control over streaming UI, token cost tracking, rate-limiting, and fallback models when an API goes down.

WHY FOUNDERS COME TO ME

Demos are easy.You already know this.
THE WRAPPER PROBLEM

Your app is just a brittle prompt.

Anyone can connect to the OpenAI API in an afternoon. But turning that into a defensible SaaS with user accounts, subscription billing, and real UX is a massive engineering lift.

Real SaaS Architecture
THE RELIABILITY

The AI hallucinates in production.

Your local Streamlit demo works great, but real users are getting unformatted JSON and rate-limit errors. You need proper evaluation, fallback logic, and streaming UI.

Structured Outputs
THE PERFORMANCE

Waiting 15 seconds for a response.

Users will close the tab before your AI finishes thinking. You need Vercel AI SDK streaming, optimistic UI updates, and intelligent caching to make it feel instant.

Sub-second perceived latency

WHAT I BUILD WITH

Optimized for latency.No hand-offs required.

From database to deployment. I own the whole thing.

FRONTEND
Next.js 15
React 19
Vercel AI SDK
AI MODELS
OpenAI
Anthropic Claude
Local LLMs
BACKEND
Node.js
Server Actions
Stripe Billing
DATA
PostgreSQL
Redis Caching
Prisma

HOW IT WORKS

From prompt to product.

We turn your core AI mechanic into a monetizable platform.

01

LLM Pipeline & Evals

Nailing the output

Before building UI, we perfect the prompts, implement Structured Outputs (JSON schema enforcement), and setup fallback models to ensure the AI behaves deterministically.

02

Streaming Infrastructure

Perceived performance

We implement the Vercel AI SDK to stream tokens directly to the client, providing immediate visual feedback to the user while the model is still generating.

03

SaaS Plumbing

Auth, DB, Payments

We wrap the core mechanic in robust user authentication, usage-based tracking (so you don't lose money on API costs), and Stripe subscription billing.

PROVEN RESULTS

TryOn Live
ShopifyTypeScript

TryOn Live

AI-powered virtual try-on Shopify app for clothing and accessories using GCP Vision API for real-time fitting.

Read Case Study→

COMMON QUESTIONS

Questions aboutalways ask me.

Scaling LLMs isn't like scaling standard APIs.

I use OpenAI's new Structured Outputs (or Anthropic's tool calling) combined with Zod validation on the server. If the LLM hallucinates a bad schema, the server catches it and retries before it ever breaks the client UI.

Absolutely. I implement middleware that counts prompt and completion tokens for every request, logs it to your PostgreSQL database, and enforces tier-based rate limits so a single user can't run up your OpenAI bill.

Rarely in production. LangChain is great for prototyping, but its abstractions make debugging incredibly difficult and add unnecessary latency. I prefer writing direct, transparent API calls and using the Vercel AI SDK for streaming.

READY?

Let's buildsomething real.

30 minutes. No pitch. No pressure. Just an honest conversation about your project and whether I can actually help.

✓ Free 30-min call✓ No commitment✓ You'll know after 1 chat