Full-stack AI products.

The whole product around the model — API, database, and the front end people use.

Start a project How I build it
  • FastAPI
  • PostgreSQL
  • Tailwind CSS
  • Streamlit

The problem

Most AI projects stall between the notebook and the product. The model works, but nothing around it is ready: no login, no database, no screen a customer can use.

Here’s how I build it

How I build it.

5 steps from the first conversation to launch. The diagram fills in as each part gets built.

  1. Step 01

    Start from the user

    We start with the person using it: what they do on each screen, and where the AI actually saves them time.

  2. Step 02

    Build the backend

    A FastAPI or Flask backend serves the model and everything around it, with authentication and rate limits from the first version.

  3. Step 03

    Design the data

    The schema goes on PostgreSQL, MongoDB or Redis, depending on the data. User records, conversation history and cached results each get the store that suits them.

  4. Step 04

    Put the model inside

    The model, agent or RAG pipeline runs behind the API like any other service. The rest of the product doesn’t need to know which model it is.

  5. Step 05

    Build what people use

    A React or Next.js front end with streaming chat and live dashboards, and internal tools in Streamlit or Gradio for your team.

What you get.

All of it, or only the part you need. Each piece can be scoped on its own.

Built with

  • FastAPI
  • PostgreSQL
  • Tailwind CSS
  • Streamlit
  1. 01FastAPI or Flask backend with authentication and rate limiting
  2. 02Schema design on PostgreSQL, MongoDB or Redis
  3. 03React or Next.js front ends with streaming chat and live dashboards
  4. 04Internal tools and quick prototypes with Streamlit or Gradio

Where I’ve done it.

Quoted from my CV, then the builds you can open and check for yourself.

  • Padelos

    Associate AI Engineer

    Jan 2026 — Present

    • Manage agent memory with vector stores (FAISS, ChromaDB) for retrieval, and Redis and PostgreSQL for short-term and persistent state.
    • Migrated the platform chatbot from Azure to AWS EC2 and shipped services with Docker and Bitbucket Pipelines CI/CD, adding CloudWatch-based observability.
  • UH

    University of Haripur

    BS Computer Science — CGPA 3.79 / 4.0

    Sep 2021 — Jul 2025

    • Built a multimodal RAG system combining text and image understanding with LangChain, FAISS/ChromaDB and Hugging Face Transformers, served through a FastAPI backend for quiz generation, Q&A and mock-interview simulations.

Builds.

All projects

Questions clients ask.

Not covered here? Ask me directly.

We already have a model. Can you build the product around it?

Yes. The model goes behind an API, and I build the backend, the database and the front end around it.

Can we start with a prototype?

Yes. A Streamlit or Gradio prototype is a quick way to test an idea with real users. What works then moves into the full product.

Which database will you use?

PostgreSQL for most product data, Redis for caching and short-term state, and MongoDB when the data has no fixed shape. Often it’s more than one.

Do you build the front end too?

Yes, in React or Next.js, including chat replies that stream in as they’re written and dashboards that update live.

Other services.

Work with me

Have a project like this?

Tell me what you want to build, and I'll tell you how I would approach it.

Let’s talk