AI Architect

I build AI that has to work outside a demo: voice assistants on government kiosks, models small enough to run offline, and fraud detection for banks. I train the models and run the GPUs myself.

Smart card renew karwana hai?
Ground floor. Bring your old card.
ID scan
Queue token
Record lookup
Token
B-142
Teacher · Gemini
Student · Qwen3-1.7B
Urdu word error rate
10%
APPROVED · 4-EYES
Cloud L4
On-prem L40S

Voice agents that act

Speech-to-speech agent, 20+ tools, self-hosted avatar

97.8% on unseen spoken turnsUrdu word error rate 90% → 10%0.99 ROC-AUC fraud model25 fps self-hosted avatar2,439 citizen messages in five daysNVIDIA T4 · L4 · L40SCloud and on-premises GPU inferenceGovernment · FinTech · Education · Healthcare97.8% on unseen spoken turnsUrdu word error rate 90% → 10%0.99 ROC-AUC fraud model25 fps self-hosted avatar2,439 citizen messages in five daysNVIDIA T4 · L4 · L40SCloud and on-premises GPU inferenceGovernment · FinTech · Education · Healthcare

What I work on

I write the code, train the models and run the servers myself.

Voice agents

Speech-to-speech assistants that call real tools: sign-ins, queue tokens, record lookups, payments.

Fine-tuning and distillation

LoRA and QLoRA on Apple MLX and NVIDIA CUDA. Big models teach small ones that run offline.

Talking avatars

Self-hosted lip-sync streamed over WebRTC at 25 fps. It stops talking when you interrupt it.

GPU deployment

NVIDIA T4, L4 and L40S, in the cloud or on a server with no internet access.

Evaluation

Test suites of a thousand cases, judged by a model. A checkpoint that regresses does not ship.

Compliance AI

AML and fraud models whose reports a person signs off before anything is filed.

Featured work

Four AI systems from the last year

Every number below comes from the work itself. Each one links to the full case study.

01National IT Board

A voice assistant for government service kiosks

People ask out loud, in Urdu or English. It answers from the official catalogue, issues queue tokens and looks up records, and an avatar I host myself speaks the reply.

services across 13 departments
69
services across 13 departments
self-hosted talking avatar
25 fps
self-hosted talking avatar
to stop talking when interrupted
85 ms
to stop talking when interrupted
Read the case study
02National IT Board · built over a weekend

The same assistant, offline on one GPU

Gemini taught two small models: a LoRA-tuned Qwen3-1.7B that picks verified answer clips, and a fine-tuned Whisper. Nothing touches the internet at run time.

acceptable answers on unseen spoken turns
97.8%
acceptable answers on unseen spoken turns
Urdu word error rate, down from 90%
10%
Urdu word error rate, down from 90%
median time to start speaking
1.19 s
median time to start speaking
Read the case study
03National IT Board · WhatsApp

A WhatsApp helpdesk for a national fuel subsidy

A Gemini agent that answers texts and voice notes in nine languages and scripts, files complaints with a reference number, and never drops a message during a deploy.

messages in the first five days
2,439
messages in the first five days
citizens helped, 33 complaints filed
252
citizens helped, 33 complaints filed
median time to reply
23 s
median time to reply
Read the case study
04Compact Machines · FinTech

AML, fraud and FINTRAC reporting

Graph models find laundering patterns. A self-hosted LLM drafts each report from cited evidence, and a person signs it before anything is filed.

ROC-AUC on fraud detection
0.99
ROC-AUC on fraud detection
recall at a 0.1% false-positive rate
84.5%
recall at a 0.1% false-positive rate
on every report before filing
4 eyes
on every report before filing
Read the case study

How I work

Roughly the same four steps on every AI project.

01

Find the real task

I sit with the people who will use it and collect real examples. Those examples become the first test set.

02

Pick the smallest model that works

A hosted model to prove the idea, then a smaller one I can run myself if cost, privacy or offline use calls for it.

03

Show something working early

A working demo in the first days, then a run against the test set at the end of each one.

04

Measure before shipping

Every release is scored first. If a new version is worse at something the old one did well, it waits.

About

I lead the AI Wing at the National IT Board in Islamabad. Before that I ran engineering at a FinTech AI startup in Calgary and an EdTech company in Riyadh, and spent six years on healthcare and distributed systems software. Eight years in, I still write most of the code myself.

Working on something with AI in it?

Tell me what it needs to do. I'll tell you how I would build it.

Book a 30-minute call