AI Engineer

Agentic Systems

Afnan Arif, AI Engineer & Automation Builder

Hello, I'm

Afnan
Arif

AI Engineer & Automation Builder

I build AI systems that do real work — research agents that check their own sources, assistants that answer from your documents, and automations that handle routine business tasks without supervision.

Available worldwide

Systems built
6
Tools & frameworks
28
Years building
2+

What I actually do

Islamabad, Pakistan

I'm Afnan. I build agentic AI systems from Islamabad — multi-agent graphs, retrieval pipelines over private documents, and automations that run business operations without anyone watching them.

Most of the work is the unglamorous half. My first research system produced confident, well-formatted reports that were quietly wrong. Fixing that meant scoring sources for credibility, checking each claim against the real text, and capping how many times it's allowed to retry. It now sometimes tells me it couldn't confirm anything — and that's the version I trust.

I also run a small AI studio called ZENTIX with two other engineers. Everything on this page I designed and built myself.

Education

  • BS Artificial Intelligence

    COMSATS University Islamabad

    2024 – 2028

Skills

  • LangGraph
  • CrewAI
  • Gemini
  • FastAPI
  • Python
  • Next.js
  • JavaScript
  • TypeScript
  • Java
  • C++
  • GitHub
  • Postgres
  • Oracle
  • MongoDB
  • Pinecone
  • Docker
  • VS Code
  • Claude Code
  • Make
  • Antigravity
  • Ollama
  • Vercel
  • Colab
  • Hermes Agent
  • React
  • n8n
  • Webhooks
  • LangChain

Projects

  • University Course Scheduler, interface screenshot

    University Course Scheduler

    Optimization

    Builds a term's timetable from the courses, rooms, and teaching hours available. It works out where two classes would collide or a room would be double-booked, and rearranges around it instead of handing back a clash for someone to fix by hand.

    View repository, opens in a new tab
  • Collaborative Research & Report Agent, interface screenshot

    Collaborative Research & Report Agent

    Agentic AI

    Several agents split a research question between them — one gathers sources, one drafts, one checks each claim back against the original text — and pass the work between each other until the report holds up. When a claim cannot be confirmed, it stays out.

    View repository, opens in a new tab
  • Automated Real Estate Valuation, interface screenshot

    Automated Real Estate Valuation

    Machine Learning

    Estimates what a property is worth from its size, location, and features, learned from past sales in the same area. It also shows which of those factors moved the estimate up or down, so the number is not just asserted.

    View repository, opens in a new tab
  • Handwritten Digit Recognizer, interface screenshot

    Handwritten Digit Recognizer

    Computer Vision

    Draw a digit and it reads it back. A convolutional network trained on handwriting samples, with its confidence shown next to the answer — so a messy 7 it is unsure about looks different from a clean one.

    View repository, opens in a new tab
  • E-Commerce Sentiment Analyzer, interface screenshot

    E-Commerce Sentiment Analyzer

    NLP

    Reads product reviews and sorts them by what the customer actually felt, then groups the complaints together so the issue that keeps coming up is visible without reading every review.

    View repository, opens in a new tab
  • Housing Society Management, interface screenshot

    Housing Society Management

    Web Application

    One place for running a residential society: who lives on which plot, what each household owes and has paid, and the complaints that are still open — so the office isn't reconciling a ledger, a register, and a stack of notes by hand.

    View repository, opens in a new tab

How I Work

  1. 01

    Discover

    I map how the work actually happens today, then find the parts worth automating.

  2. 02

    Architect

    System design first — what connects to what, and where the data moves — before any code.

  3. 03

    Build

    Iterative, with working demos along the way. You see it running before it ships.

  4. 04

    Deploy

    Live, with logging and error handling, so failures surface instead of hiding.

Every project starts with understanding the workflow — not the tech. The right architecture comes from the problem, not the trend.

Afnan

Let’s build something that actually ships

Let’s work
together

If there's a workflow someone is holding together by hand, or a pile of documents nobody has time to read — that's the shape of problem I'm good at. Tell me what you're building and I'll tell you what's realistic, including when the answer is that you don't need AI for it.

Start a conversation

© Afnan Arif