I'm Atif Iqbal, a fullstack and AI engineer based in Lahore, working with clients worldwide. I build booking platforms, SaaS products and AI tools that help teams find information and automate everyday work.
With over 10 years of software delivery experience, I take projects from architecture through launch, documentation and ongoing support.
A decade of shipping client platforms, now paired with the AI systems that make them smarter.
I have spent the past decade building and shipping production software for clients across the UK, the USA, Australia, the UAE and Pakistan: booking marketplaces, learning platforms, cryptocurrency exchanges, payment products and multitenant SaaS. My work sits at the architecture level, covering the data model, the API surface and the infrastructure it runs on, then owning the build through launch and everything that comes after it.
For the past few years that practice has extended into applied AI. I design retrieval-augmented generation pipelines over private corpora, tune embedding and chunking strategies against real query sets, run vector databases under production load, finetune small language models where cost and latency matter more than raw capability, and build agentic commerce systems where an agent, not a form, completes the journey.
What has not changed is the standard. Systems that hold up under real traffic, integrate cleanly with how the business actually operates, and are handed over documented and maintainable. I care far less about which framework is fashionable this year than about whether the thing is still earning its keep two years after launch.
System design, data models and API contracts, then owning the build from first commit through launch and scale.
RAG pipelines, vector search, finetuned small models and agents that complete real tasks, not demos.
Payment rails, CRMs and multitenant SaaS designed around the business outcome, not the tech stack.
Six things I build for clients. Each one links to the writeup or the shipped project it comes from, so you can check the claim rather than take it.
I build assistants that retrieve relevant information, cite their sources and flag questions the available evidence cannot answer. That means the whole path: ingestion, chunking, hybrid retrieval, reranking and grounding, with recall measured against a golden set rather than judged by impression.
Retrieval quality is decided long before the LLM is called. I choose and evaluate embedding models, tune chunk geometry, and measure recall on real queries rather than guessing.
Running vector search in production means index tuning, metadata filtering and honest latency budgets. I ship it on Postgres when that is enough, and on a dedicated engine when it is not.
Not every task needs a frontier model. On narrow, high-volume work a finetuned 3B–7B model can cut cost and latency sharply where an eval set shows quality holds, and it can run on your own hardware.
Commerce is moving from pages to agents. I build the tool layer for catalogue, cart and checkout, so an agent can complete a purchase safely with a human in the loop where it counts.
Demos are easy; reliability is the work. Multiagent graphs with typed handoffs, golden-set evaluation on every change, and guardrails against injection, leakage and quiet regressions.
A clinical AI assistant, a tourism platform with a citing concierge, a UK enrolment system, a mining ERP, a cross-border payments product and an onchain marketplace. Each one leads with the problem it had to solve.
Tell me the problem and roughly where you are with it. You will get a straight answer on whether I am the right fit, a scope and a price, usually within a day.
What I have learned shipping Laravel platforms and AI systems: retrieval, agents, small models and the unglamorous parts that decide whether any of it works.
The tools I reach for daily. Depth on the AI side lives on the AI Engineering page; every project lists the stack it was actually built on.
Whether it is a web platform, a RAG assistant over your own data, or an agent that can actually transact, tell me what you have in mind.
A short call to understand the problem, the constraints you are working under and what success actually looks like.
A written scope with milestones, a timeline and a fixed price or day rate, so there are no surprises later.
Weekly demos against a live staging environment. You see working software, not status reports.
Documented code, a deployment runbook and a support window after launch. The system stays yours.
Helps me reply faster