Question 1

Do you train or fine-tune any model on our data?

Accepted Answer

No. Your data runs your system; it never trains a shared model that anyone else can reach. Where we fine-tune, the resulting weights are yours and stay inside your environment.

Question 2

Where does our data physically sit, and can it leave the UAE?

Accepted Answer

Inside UAE borders, on the Core42 sovereign cloud and Microsoft Azure UAE regions. Nothing crosses the border without your written approval.

Question 3

When a prompt reaches a model, who can technically see it?

Accepted Answer

For hosted models we use enterprise endpoints with zero data retention, so the provider neither keeps nor trains on your prompts. For the most sensitive work we run open models inside your environment, and nothing leaves it.

Question 4

Is our data encrypted, and who holds the keys?

Accepted Answer

Yes, encrypted in transit and at rest. You can hold your own encryption keys, so access to the data depends on keys you control, not ones we keep.

Question 5

How do you stop your own operators from seeing our production data?

Accepted Answer

Access is role-based, least-privilege, and logged. Operators reach only what their role requires, every touch is on the audit trail, and you can revoke access at any time.

Question 6

Are you compliant with UAE PDPL?

Accepted Answer

We design to UAE PDPL from day one, with a data-processing agreement, data residency controls, and data-subject request handling built into the system rather than bolted on later.

Question 7

Which standards do you build to?

Accepted Answer

ISO/IEC 27001 for information security, ISO/IEC 27701 for privacy, ISO/IEC 23894 for AI risk, SOC 2 for controls, and the NIST AI Risk Management Framework. They cover the model layer alongside the hosting.

Question 8

Will this survive a regulator's audit?

Accepted Answer

Every decision carries a timestamped trail: the data it used, the model version, and who approved it. We built ComplyBase on exactly this for UAE financial institutions answering to the Central Bank and ADGM.

Question 9

Can you explain to a regulator why the AI decided something?

Accepted Answer

Yes. Explainability is part of the build, so a flag or a decision arrives with its reasoning and the sources behind it.

Question 10

Who keeps the system compliant when the rules change?

Accepted Answer

We do, as part of running it. Regulatory changes flow into the rules and models under our managed operation, so compliance is maintained rather than frozen at launch.

Question 11

What happens when the AI is wrong?

Accepted Answer

A person owns every consequential decision. The system routes uncertain or high-stakes cases to a human, with the reasoning attached, so a wrong output is caught before it acts.

Question 12

What accuracy can we actually expect?

Accepted Answer

We measure it on your data against a scorecard we agree up front, and report the error rate openly. We would rather quote a number you can hold us to than call it highly accurate.

Question 13

How do you stop it hallucinating on high-stakes work?

Accepted Answer

Answers are grounded in your own sources through retrieval, each claim cites where it came from, and the system abstains when confidence is low instead of guessing. When it draws on public data, it triangulates across independent sources before it relies on a claim.

Question 14

Where is the human in the loop?

Accepted Answer

At the points that carry legal, financial, or safety weight. We map those checkpoints during design, so oversight sits where a wrong call would cost you most.

Question 15

How do you catch the system drifting after launch?

Accepted Answer

We monitor accuracy in production, retrain as the data shifts, and report against the scorecard, so quality decay surfaces on our watch, not yours.

Question 16

Will our people actually use it, or will it sit unused like the last tool?

Accepted Answer

We design around the work your people already do and bring them in during the build, not at go-live. Adoption is a metric on the scorecard, tracked and reported like any other outcome.

Question 17

Is this going to replace our people's jobs?

Accepted Answer

The systems take the repetitive research and paperwork off your team so they spend their time on the judgement calls. We build for people to run the work with the system, and shape the roles around it with you.

Question 18

How do you train our team and hand over knowledge?

Accepted Answer

Enablement runs across every layer of the build, with runbooks, documentation, and hands-on sessions, so your team can operate and extend the system instead of depending on us for every change.

Question 19

What do you need from us for this to succeed?

Accepted Answer

Access to the problem, the data, and the people who own the process, plus a decision-maker who can clear blockers. We keep the demand on your team light and scoped.

Question 20

How do you win over a team that's sceptical of AI?

Accepted Answer

We start with the work they already know and keep the system's reasoning and sources visible, so people can check an answer instead of trusting it blind. Adoption follows when the tool takes on the work they least want to do and shows why its output holds up.

Question 21

How do you track ROI?

Accepted Answer

After the build, we define the KPIs that matter with you and stand up a dashboard that tracks them live. It generates the reports your team needs, so progress shows up in the numbers you chose rather than a story we tell.

Question 22

Will this connect to the systems we already run?

Accepted Answer

Yes. We integrate with your CRM, ERP, core banking, data warehouse, and identity provider, inheriting your single sign-on and access rules instead of replacing them.

Question 23

How long before we see real value?

Accepted Answer

You see a working MVP early in the build. We agree the smallest slice that proves value coming out of design, and the build delivers that first.

Question 24

What does it cost to run, not just to build?

Accepted Answer

We separate build from operate and show both before you commit. The running cost is usage-based: you pay for the tokens the system actually processes, so it tracks your real volume and scales up or down with it, rather than a flat licence fee.

Question 25

After launch, who runs it and what do we get?

Accepted Answer

We do: 24/7 monitoring, retraining, SLAs on uptime and response, and continuous compliance. The team that scoped and built the system is the team you call.

An AI-native company built to deliver.

Principles, and proof.

The application is the deliverable, and we own every layer beneath it.

Every model. Every system.

Seven phases, end to end.

Discovery & Assessment

Where we put AI to work.

Smarter finance, faster decisions

From production to point of sale

Smarter government, better outcomes

Resilient operations, end to end

Know demand, serve at scale

Systems we built, live in production.

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Let’s put sovereign AI to work.

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