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Ekrami Labs

About

The studio behind the work.

An independent AI Research & Engineering Studio, built on three decades inside the rooms where enterprise IT is actually operated.

Ekrami Labs is the work of an engineer who spent thirty years inside the systems enterprises actually run — not the systems they demo. Oil & gas platforms. Hospital networks. Manufacturing lines. Telecommunications core. The unglamorous places where technology either keeps the operation running or becomes the cause of the incident report.

That career produces a particular kind of instinct. It treats AI the way a control-room engineer treats a new instrument — with curiosity, caution, and the understanding that a model which cannot explain itself is a liability rather than an asset.

Why independence

The studio is independent on purpose. Independence is what lets the work stay loyal to engineering rather than to a product narrative. It is what makes it possible to walk away from a brief that does not deserve to be written, and to keep working on a thread long after the launch window closes.

What we focus on

Enterprise AI architecture. Governance that ships with the system. Retrieval-augmented systems designed against real document corpora. Bounded agentic workflows inside the security operations centre. Practical AI that earns its place inside regulated environments.

How we work with clients

Engagements begin with a conversation, not a contract. We spend the first sessions listening: to the operators, to the audit committee, to the on-call engineer. The artefacts we ship — index designs, evaluation harnesses, governance playbooks — tend to be unglamorous and durable.

Trajectory

A career written in systems.

The studio is built on three decades inside the rooms where enterprise IT is actually operated. Each step informed the one after it.

  1. Foundations

    Electrical Engineering

    Began with the physics of signals and circuits — the discipline of making systems behave the way the schematic says they should.

  2. Network Era

    Enterprise Networks

    Moved into the rooms where uptime is measured in nines and the cost of a misconfiguration is a regional outage.

  3. Operations

    IT Leadership

    Led IT organisations across healthcare, manufacturing, telecommunications, and energy — learning how technology either serves the operator or becomes another obstacle.

  4. Oversight

    Board Leadership

    Served on boards and audit committees, where decisions stop being about tools and start being about consequences.

  5. Transformation

    Digital Transformation

    Spent a decade turning legacy estates into modern platforms without ever treating continuity as optional.

  6. Today

    Enterprise AI

    Applied everything above to the design and deployment of AI systems that have to earn their place inside a regulated enterprise.

  7. Forward

    Research

    Returned to first-principles research — the work of asking whether the systems we ship are the systems we would defend in front of an auditor.

Credentials

Certified, not just claimed.

A working list of the credentials behind the AI, cloud, network, and security foundations the studio builds on.

AI & Data Engineering

  • Azure Data Engineer Associate (DP-203) Cert Prep — Design & Develop Data Processing· Microsoft Press / LinkedIn (2024)
  • Python for Data Engineering: from Beginner to Advanced· LinkedIn Learning (2024)
  • Hands-On Introduction: Data Engineering· LinkedIn Learning (2024)
  • GPT-4 Foundations: Building AI-Powered Apps· LinkedIn Learning (2023)
  • Generative AI Imaging: What Creative Pros Need to Know· LinkedIn Learning (2023)
  • Generative AI Skills for Creative Content: Opportunities, Issues, and Ethics· LinkedIn Learning (2023)
  • Microsoft Azure for Data Engineering· Coursera (2023)
  • Data Storage in Microsoft Azure· Coursera (2023)

Cloud & Infrastructure

  • Linux: Bash Shell and Scripts· LinkedIn Learning (2023)
  • Azure Administration Essential Training· LinkedIn Learning (2022)
  • Kubernetes: Microservices· LinkedIn Learning (2022)
  • Kubernetes: Native Tools· LinkedIn Learning (2022)
  • Learning Kubernetes· LinkedIn Learning (2022)
  • Docker Essentials: A Developer Introduction· IBM (2022)
  • PowerShell: Automating Administration· LinkedIn Learning (2022)
  • Learning PowerShell· LinkedIn Learning (2022)
  • Learning Virtualization· LinkedIn Learning (2022)
  • Learning the Elastic Stack· LinkedIn Learning (2022)
  • Server Administration Essential Training· LinkedIn Learning (2022)

Networking & Operations

  • Networking and Administration Fundamentals· LinkedIn Learning (2022)
  • NetOps (DevOps for Network Engineers): Automating Networks· LinkedIn Learning (2022)
  • Networking Foundations: Networking Basics· LinkedIn Learning (2022)

Security, Governance & Process

  • Information Security Management System (ISMS)· Vision / IMI (2009)
  • Business Modeling and Business Process Reengineering· TÜV (2008)
  • Business Process Analyst· Vision (2008)
  • Certified Ethical Hacker (CEH)· Kahkeshan Noor (2006)

Philosophy

What we work to.

Four principles guide every project, every research thread, every customer conversation. They are written down so they can be held to.

Engineering

Software is built, not declared. Every claim we make is backed by a system, a test, or a document that can be examined.

Integrity

We design for the audit. Every model decision, every data flow, every operator action is traceable from day one — not bolted on after a regulator asks.

Innovation

Innovation without engineering is theatre. We adopt what is mature, adapt what is emerging, and reserve research for problems worth solving twice.

Practical AI

Useful, deployed, quietly improving the work of the people who use it. The best AI in production is the AI no one has to think about.