AI Engineer

Rayyan
Mohsin

Agents, vision pipelines, generative video — plus the unglamorous plumbing that keeps them alive: auth, rate limits, retries. Almost everything on this page is deployed and clickable. Currently at ClientAcquisition.io, working on TryCook.ai.

About

I like the practical end of AI — the part where a model has to survive real users, real data, and a production budget.

Most of it is agents doing jobs that used to need a person — managing ad campaigns, cutting demo videos, grinding through research — with enough guardrails to be trusted with real money. The long tail of experiments lives on GitHub.

Muhammad Rayyan Mohsin
Pakistan · UAE

2,000+

Clients Served

10

Products Shipped

40+

Tools in One Agent

Proof of Work

View all →
Project 1 of 6: Meta Ads Manager
01 / 06Active

40+ Tools

LLM agent

Meta Ads Manager

Facebook/Instagram ad management run through an LLM agent — it creates campaigns, generates creatives, and adjusts performance over the Meta Graph API. The hard problems turned out to be the boring ones: OAuth, token encryption, rate limits, and getting 40+ tools to behave.

02 / 06Live

<60s

render time

Launchable

Describe your product in chat and it comes back as a 1080p demo video about a minute later — an agent writes the scenes, picks the type and motion, and keeps everything on brand. It started as a question: how much of a video editor can one good agent replace?

03 / 06Live

9 Concepts

from one URL

Halftone

Paste a product URL and it reads the page into a brand kit — audience, offer, palette, tone — then stages nine ad concepts on a canvas, rendering images on demand. Most of the work is in the guardrails: type, color, and crop rules enforced at generation time, and budget caps so a session can't quietly burn money.

04 / 06Client Work

3-Model Pipeline

computer vision

TenCount

Fight footage in, punch analytics out. Three models chained together: fine-tuned YOLOv11m tracks the fighters, YOLOv8m reads their pose, and an AttentionBiLSTM classifies six punch types from the motion. Built to replace hours of manual fight review.

05 / 06Live

3 Sources

Reddit · YouTube · web

Agent Memory

Research agents that scrape Reddit, YouTube, and the web, then file what they find. Everything stays searchable in plain English through RAG over vector embeddings — so nothing your team already learned gets researched twice.

06 / 06Live

Side Project

for peers

CV Maker

A no-friction CV builder I shipped on a weekend for friends and juniors at university applying for their first internships and jobs. Pick a template, fill the form, export a clean PDF — no signup, no paywall. Built because the existing tools felt hostile to first-time job seekers.

Internal Tooling

Client intelligence · ClientAcquisition.io
Internal

The Brain

Our client roster lived only on the platform where we talked to clients — nowhere queryable. I turned it into a data pipeline feeding an optimized Obsidian clone: one force-directed graph — every client a node, sized by contract, colored by health. Click a node and the whole file opens. A Hetzner-hosted agentic layer re-parses and normalizes the data daily, so it never goes stale.

On top sits a RAG agent: the team asks in plain English — “which clients are at risk and why?” — and gets a grounded answer instead of digging through files. It reshaped how the team works: account reviews take minutes instead of combing scattered notes, everyone operates from the same up-to-date picture, and at-risk clients surface before they become fires — a more organized, more efficient workflow.

191

Clients, one graph

Daily

Agentic refresh

RAG

Ask, don't dig

UGC Ad Pipeline

Nothing in this video was filmed. A pipeline turns one photo and a short voice note into a finished, post-ready vertical ad — cloning the voice, driving a talking-head, generating the b-roll, and assembling the captions, cutaways, and timing automatically. Each cut then gets an automated review pass before export.

This clip is the real output, built from a single headshot and a 40-second voice sample.

Where I've Built

Daily Drivers

TensorFlow

PyTorch

OpenAI

React

Next.js

TypeScript

Python

Docker

PostgreSQL

Redis

Node.js

Supabase

LangChain

Vercel

Tailwind CSS

FastAPI

MongoDB

GitHub

TensorFlow

PyTorch

OpenAI

React

Next.js

TypeScript

Python

Docker

PostgreSQL

Redis

Node.js

Supabase

LangChain

Vercel

Tailwind CSS

FastAPI

MongoDB

GitHub

Open to new work

Building something?
I'll take the AI part.

Available for full-time, contract, and founding-engineer roles. Email reaches me fastest — a 30-minute call works too.