Data & AI

What this practice does

We turn enterprise data into decisions — building the platforms that make data trustworthy, the analytics that make it useful, and the AI that acts on it. For institutions in regulated sectors, that work is shaped from the outset by where data is allowed to live and who is permitted to see it.

Where engagements usually start

• A data estate that has grown by accumulation rather than design, with no single trustworthy view
• Reporting that takes days when decisions need hours
• Data spread across systems that cannot, for policy or practical reasons, be copied into one warehouse
• An AI ambition that stalls the moment security, residency or the regulator enters the conversation
• Models built in isolation that never reach production

What we deliver

• Enterprise data and analytics platforms
• Data virtualisation and logical data fabric
• Artificial intelligence and generative AI solutions
• Advanced analytics and executive dashboards
• Machine learning and predictive intelligence
• Data engineering and integration services
• Conversational AI and virtual assistants
• Enterprise reporting and business intelligence
• AI governance and responsible AI frameworks

Data virtualisation with Denodo

Not every data problem is solved by moving data. We design and implement Denodo-based access layers that let teams query governed data where it already lives, rather than copying it into yet another store. For institutions in the Kingdom that matters twice over: it shortens the path from question to answer, and it avoids creating new copies of sensitive data that then have to be secured, classified and justified to a regulator.

The work typically covers modelling a logical layer over core banking, CRM and operational systems; applying row- and column-level security consistently at the virtual layer rather than re-implementing it per consumer; publishing governed data products to analytics and AI consumers; and tuning query performance so the abstraction does not cost more than it saves.

On-prem and air-gapped by default

Our default posture is that data, models and intellectual property stay inside your perimeter. That shapes the architecture: GPU capacity you control, retrieval that respects existing access rights rather than flattening them, observability over what was asked and answered, and a lifecycle plan for the day a model needs replacing. It is more work than calling an API — and for many institutions it is the only route that is permissible at all.

Governance is part of the build

Responsible AI frameworks, model documentation and human-in-the-loop design are delivered alongside the system, not retrofitted once someone asks how it makes decisions.

Technology we work with

Denodo for data virtualisation and the logical data fabric. CrewAI for orchestrating multi-agent systems, deployed inside client infrastructure including on-premises and air-gapped environments. Beyond those, platform choices follow the estate and the constraints it operates under.

Regulatory alignment

Data work is delivered against NDMO expectations on classification, residency, sharing and governance, and against PDPL on privacy by design, data lifecycle and consent. Where the client is a financial institution, SAMA frameworks shape the controls. AI systems are documented so their behaviour can be explained to a reviewer rather than defended after the fact.

Beyond the practice

Our product studio, IOTA Labs, ships enterprise AI products built on the same principles — Qantara, iChat, iCompliance and iInvestmentHouse.