AI entrepreneur and product engineer

Umair MajeedFounder and CEO, Datics AI

When a software company wants AI in a product its customers already rely on, I do not start with the model. I start with the product.

I ask what customers are trying to do, what the AI needs to see, what it should be allowed to do, and where a person has to stay in control. Then we build only what that first capability needs, inside the workflows, data and permissions that already work. I call this Brownfield AI.

My brother Umar and I started Datics AI in Lahore in 2018, and we grew it from two people to a team of around sixty-five. I led the technical build of a U.S. property-tax platform from launch to acquisition, served on the Prime Minister's Advisory Council on IT, and received national awards for the company and for my own work. Today I lead Datics from Austin, Texas.

Work with me Invite me to speak

Recognized, featured or trusted by

Prime Minister's Advisory Council on IT
Lahore Chamber of Commerce & Industry
PTV Home
Web Summit
Collision
MWC Barcelona
USAID
Punjab Information Technology Board
Amazon Web Services
Upwork Expert-Vetted

How I approach it

AI in a product that already works

Start with the customer problem, not the model

I do not ask where we can add AI. I ask what users are repeatedly trying to understand, decide or do that the product could make substantially easier.

Start with the software that already works

Established products will not be rebuilt around AI overnight. They will evolve. I keep what should stay and change only what the new capability actually requires.

Decide what the AI can see and do

Answering a question is one thing. Updating a record, messaging a customer or triggering another system is very different. I decide what the AI needs access to, what it may do, and where a person reviews or approves.

Production is the real test

A prototype proves the AI can do something. Production proves the product can safely depend on it, with the right data access, permissions, human approval and audit trail.

Build capabilities, not experiments

Every AI feature should make the next one easier to build. Otherwise a product collects AI experiments instead of becoming AI-native.

The five questions I ask first More of my thinking

Selected work

Shipped into real products

We built these at Datics AI, where I lead the team as CEO. Clients are not named: we show the work, not the client.

Brownfield AI · Dental and veterinary SaaS

AI communication intelligence inside a live practice platform

The problem: An established communication platform for dental and veterinary practices was losing appointments to missed calls and incomplete follow-up.
What we built: Call intelligence, AI engagement and booking workflows, connected to several practice-management systems through the product's existing integration and permission model, in a HIPAA-aligned, customer-controlled Google Cloud environment.
The result: In a documented example, 311 appointments recovered across six locations, with no rebuild of the product.

Read the case study

AI product · Healthcare SaaS

An AI intake prototype taken to production multi-clinic SaaS

The problem: A working GPT-powered patient-intake prototype had to become a product that many clinics could safely run.
What we built: Adaptive intake and structured clinical summaries, wrapped in clinic isolation, role-based access, onboarding, provider configuration and billing, on HIPAA-aligned Microsoft Azure hosting.
The result: The intake capability now runs as a multi-clinic product with the access control, onboarding and billing a healthcare SaaS needs.

Read the case study

Clinical AI · Human in the loop

AI clinical documentation inside the dentist's appointment

The problem: Dentists were spending time after each appointment turning the conversation and their notes into structured clinical documentation.
What we built: Live transcription (Whisper and Deepgram), patient history retrieved into context, and LangChain-orchestrated drafting of clinical notes, with a review and approval step for the dentist.
The result: Notes are drafted during the appointment, and nothing is released until the dentist approves it.

Read the case study

U.S. platform · Launch to acquisition

Secure Tax Office: from launch to acquisition

The problem: A U.S. software company needed a platform that sheriffs' offices could rely on for property-tax collection.
What we built: As fractional Chief Development Officer, I led its technical development and implementation, and the CEO relied on my recommendations on architecture and development strategy.
The result: Secure Tax Office launched publicly in December 2020, was adopted by several sheriffs' offices in Louisiana, and was acquired in December 2022.

More Datics AI case studies

Client reviews

What clients say

In Rich's words

How our partnership began, in Rich's words

Rich DeLancey is the CEO of Coast Technology in Tampa, the U.S. company behind Dealership Toolkit and Secure Tax Office. In a video message to the Datics team, he told a story his father used to tell, and explained why he chose to work with us.

The thing that impressed me about him was his willingness to do whatever it took to get the job done.

Rich DeLancey, CEO, Coast Technology (Dealership Toolkit), Tampa, Florida. From his video message to the Datics team, January 2021. Watch it

5.0 average from 49 client ratings on Upwork, with 100% Job Success and Top Rated Plus status across 66 jobs and more than 17,000 hours.

Great experience! They helped us update our website and fixed an issue that another contractor couldn't figure out. Super fast turnaround, clearly very competent, really friendly. …

Client review on Upwork · Full-Stack Web Developer Needed for Front-End, Back-End & Figma-Based Website Updates, April 2026

Umair and his team worked efficiently and smoothly with our Tax team. His work was top-notch and we couldn't be happier. I give Umair and Datics my highest recommendation.

Client review on Upwork · Tax Portal, February 2021

Umair is a proven professional who knows what it takes to get things done. He has a deep well of knowledge he can tap for tackling complex development problems. …

Client review on Upwork · Software Engineer for New Product PoC, June 2025

The team did a good job creating a new AI application with specific attributes. They were persistent and flexible with changes and corrections that arose.

Client review on Upwork · Development of AI based Process Flow Application, April 2025

What people say All reviews on Upwork

Working together

How I would approach your product

If you run an established SaaS product, know it needs AI and do not want to rebuild it, this is how I would approach it with you. I shape the plan, and our team at Datics AI builds it with your team, in three steps.

01
AI-Native Sprint
I start with a short, opinionated look at your product, users, workflows, architecture, data and permissions. You leave with a prioritized list of AI capabilities, what each one needs, the risks, and the first one to ship.
02
Unlock
We change only what that first capability requires, such as APIs, data access, permissions, retrieval or evaluation. No broad modernization for its own sake.
03
Capability waves
We ship AI into the existing product, measure it, improve it, then reuse what it left behind (context, tools, permissions, evaluations) so each next capability is faster to build.

It starts with a 30-minute AI product review: where AI can create value in your product, what may block it, and the best next step.

Book a 30-minute AI product review Email me

Insights

Latest writing

October 20, 2026

Eight years in: growth as clarity

Datics AI started with AI and data science in 2018. Eight years later, the biggest lesson is that growth does not always mean becoming broader.

October 14, 2026

A prototype proves the AI can do something

A working AI prototype tells you surprisingly little about whether it can ship. Production asks a different question: can the product safely depend on it?

October 13, 2026

Five questions before approving an AI initiative

If I were approving AI work inside an established SaaS product, I would not start with the model. I would start with these five questions.

All insights

Track record

Eight years of proof

Most of this recognition came in Pakistan between 2020 and 2023, while I built Datics in Lahore. Since August 2024 I have led the company from Austin.

8 yearsBuilding Datics AI, from a two-person start in 2018

65People on the Datics team by 2022, up from two

5National and international honours, 2020 to 2023

Top 1%Upwork Expert-Vetted, by Upwork's own description

500+Young people trained by Datics AI through TechLift and NAVTTC

1,000+Delegates at the first Tech Nation conference, which I founded

15Talks, keynotes, panels and hosted conferences

8Countries where Datics AI represented Pakistan's tech industry

Award