About

We make established software intelligent.

The software with the most valuable AI opportunities is rarely starting from scratch. It already has customers, workflows, permissions, integrations and years of domain logic. Datics exists to make AI work inside that reality.

We bring together applied AI, data and SaaS product engineering so established products can become AI-native without being rebuilt around the latest model.

Book a 30-minute product review See our work

The Datics intersection

Data

Product engineering

Specialization

Brownfield AI

Intelligence engineered for the product that already exists.

Founded 2018: AI & data science roots
Brownfield AI: Established products, real production constraints
AI + product engineering: One discipline around the capability

Our story

AI roots. SaaS engineering depth. Brownfield AI by specialization.

Datics did not start as a general software company that later added AI. We started in AI and data, developed the product-engineering depth required to put intelligence inside real software, and made that intersection our specialization.

2018
2018
AI and data science first
Datics was founded in 2018 around data science, machine learning and applied AI, before generative AI became a mainstream product category.
02
02
Product engineering became necessary
Clients did not need isolated models. They needed intelligence to work through complete products — applications, APIs, data, permissions, integrations and release environments. Datics built deeper SaaS product-engineering capability around that reality.
03
03
Mature software changed the discipline
Working inside products already serving customers taught us that useful AI has to respect the system that already works. Existing workflows, business logic, tenancy and permissions are not obstacles to ignore; they are the design brief.
Today
Today
Brownfield AI
That intersection became the specialization: making established vertical SaaS AI-native without forcing customers to start over.

Brownfield work is what we are built for, not something we merely tolerate.

The founders

Built at the intersection of AI and production software.

Datics was co-founded by brothers Umair Majeed and Rana Umar Majeed. Their backgrounds span AI, data science, software engineering, product architecture and company building — the combination that shaped Datics’ brownfield approach.

Umair Majeed

Co-founder & CEO

Umair co-founded Datics in 2018 and leads the company’s strategy, positioning and client outcomes. His background spans software, data and product work, with a focus on turning technical depth into product programs established software companies can adopt and operate.

Focus

Product strategy, positioning and client outcomes across established software companies
Turning technical capability into commercially useful AI product programs
Shaping Datics from AI/data roots into a focused Brownfield AI company

Rana Umar Majeed

Co-founder & CTO

Umar co-founded Datics in 2018 and leads the company’s technical strategy across AI, architecture, data and production delivery. His background spans data science and product architecture, including work on mature software systems at ESW Capital and Crossover.

Technical credibility

Data science and product-architecture experience across mature software systems
Technical leadership across AI architecture, data, permissions and production delivery
AG-UI community contributor; helped drive the AWS Strands ↔ AG-UI integration

Together, that combination defines Datics: product and commercial judgment paired with technical depth and production discipline, applied to established software where AI has to work inside the system that already exists.

How we build

Three disciplines shape the work.

AI and data science roots

We treat evaluation, uncertainty, retrieval, model behavior and data quality as engineering problems — not demo polish.

SaaS product engineering discipline

AI has to work through the application, APIs, integrations, tenancy, permissions and release process customers already depend on.

Brownfield by choice

Existing constraints are the design brief. We change what the capability requires and preserve what already works.

See how we work →

How the team works

One accountable team around the capability.

Product, AI/data, application and production engineering work together around a defined outcome. We work alongside the client’s product and engineering leaders, inside the existing product and release process, rather than supplying interchangeable developers.

Product context first

We start from the workflow and user outcome, not the model.

Engineering across the boundary

We own the application, data, permissions and production work the capability depends on.

Built to compound

Each shipped capability should leave reusable foundations for the next one.

Explore what we do →

Proof

The specialization is backed by production work.

The strongest evidence is the software already in market: established products where AI had to work through real users, permissions, integrations, data and production constraints.

PeerlogicAI embedded into live practice workflows with measurable commercial impact.DawaAI and automation added around an existing regulated .NET / SQL product core.MIRAAn AI prototype turned into role-aware, clinic-isolated production SaaS.

See client outcomes →

Your product does not need to start over to become AI-native.

Show us where AI needs to work inside the product. We will help determine what should change — and what should stay exactly as it is.

Book a 30-minute product review