Leadership
Built by an engineer, run like a product company
I am Muhammad Ghulam Jillani, Founder and CEO, Principal Generative AI Architect at Jillani SofTech. I started this consultancy because I kept meeting the same situation from the other side of the table: a company that wanted AI, a vendor that delivered an impressive prototype, and a system that fell apart the first week real data hit it.
So the company was built around 1 rule. Nothing ships unless it works in production. Every system we deliver has documented architecture, real monitoring, a clean handover and post-launch support, as the baseline rather than an upsell.
6 years and more than 22 production systems later, I still personally lead architecture and delivery on every engagement. It also means I will talk you out of work. If a gradient boosted model on your existing tabular data solves the problem more cheaply and more defensibly than a language model, that is what the scoping document recommends, even though it is a smaller engagement for us. The reputation is worth more than the invoice.
The team
A small senior team, 2 engineers on every codebase
Jillani SofTech is a registered IT services firm. When this site says we, it means the founder as the architect on every build plus the engineers below, with no account managers or delivery layers in between.
Muhammad Ghulam Jillani
Founder and CEO, Principal Generative AI Architect
Designs the architecture, writes the core and runs the deployment on every engagement. 6+ years in production AI, Top 100 Global Kaggle Master, Claude Certified Architect.
Lead AI Engineer
Retrieval, evaluation and agent delivery
Owns the evaluation harness and the retrieval layer on client builds, and is the second engineer on every codebase so continuity never depends on 1 person.
Lead Machine Learning Engineer
Fine-tuning, classical ML and MLOps
Runs training pipelines, held-out evaluation and drift monitoring, and leads the classical ML builds where a language model is the wrong tool.
Generative AI Engineers
Build team
Engineers and interns working under the founder on backend, integrations and automation, inside the client’s repositories and review process.
You contract with a company
Master services agreement and NDA on request, USD invoicing, international bank transfer, and milestone payments agreed in writing before work starts.
Continuity is designed in
2 engineers know every codebase and documentation begins in week 1 rather than at handover.
Your time zone
Fully remote across the USA, UK, Germany and the EU. Working hours and a fixed weekly checkpoint are set to your calendar.
Verified, not self-declared
Credentials you can check from the issuer's own record
Upwork Top Rated Plus
100% job success rate with $100K+ earned across enterprise engagements, public and not editable by us.
VerifyAWS, Azure and GCP professional certificates
Covering the 3 platforms our clients run on.
Top 100 Global Kaggle Master
Competitive machine learning at the level that predates the current wave of generative tooling.
VerifyLinkedIn Top Voice in AI, 24 times
Recognised for technical writing on production AI engineering.
VerifyDeveloper programs
NVIDIA Developer Program contributor, AMD AI Developer Program member, KaggleX BIPOC mentor lead.
4 commitments
The rules we hold to even when a different answer would be more profitable for us
We will tell you when AI is the wrong answer
Plenty of problems are solved better by classical machine learning, a deterministic workflow or a change to the process itself. If that is the case for yours, the scoping document says so, and we would rather lose the build than deliver an expensive system that underperforms a simpler one.
Success is defined before code is written
Retrieval accuracy, cost per request, hours removed, error rate. We agree the measurable target in week 0 and report against it throughout, so nobody is arguing about whether the project worked at handover.
You own everything
Repositories, pipelines, infrastructure configuration and documentation live in your accounts from the first commit. There is no licensed platform underneath and no dependency on us to keep the system running.
We stay after launch
Monitoring, incident response, model tuning and quarterly improvement cycles are part of every engagement. The delivery date is when the system starts being used, and our attention continues past it.
Numbers from live production systems
Figures from deployed systems with real clients, with no projections mixed in
0%
Average process efficiency gain across all automation deployments
0%
Reduction in manual workload via AI agents and workflow automation
0%
Best-in-class RAG accuracy in healthcare and legal systems
$0K
Client costs saved and documented across all engagements
0.0%
Production uptime across monitored deployments
0+
Production AI systems delivered
- MSA + NDA
- On request, from the company
- USD
- Invoicing with international bank transfer
- 2
- Engineers on every codebase
- 30 days
- Post-launch support minimum