AI Assurance

Prove the AI is trustworthy to a Saudi review, with measurements

Evaluation harnesses, grounding and hallucination scoring, Arabic tested separately from English, bias and red-team passes over the deployed path, and an evidence pack your risk function and the regulator can read without us in the room.

The service at a glance

The problem, our work, your result

Business problem
Can you prove your AI is accurate, fair and safe?
Models are released without testing for bias, errors or misuse, and nobody can explain a decision when challenged.
What we do
We test, document and monitor your AI systems so every decision can be explained and defended.
What you receive
- Model test report
- Bias and safety checks
- Model documentation
- Ongoing monitoring

Who it is for

Built for the people who own the decision

Risk, compliance and security

Evidence for regulators.

AI teams

Clear release criteria.

CEO and executive team

Reputation protected.

Internal audit

Traceable decisions.

The goal: an AI-ready organisation

Where this service moves you

Data ready

Connected, clean, governed and allowed for AI use.

KPIs ready

Agreed measures, one definition each, a named owner.

Organisation readyThis service

Roles, decision rights and skills to adopt AI safely.

=AI that reaches production and creates value

Approach

How we work with you

01
Scope
Pick models and risk level.
02
Test
Check accuracy, bias and safety.
03
Document
Record design and limits.
04
Monitor
Watch behaviour in production.

Engagement options

Start where you are

Model review

One model tested and documented.

Assurance programme

All high-risk AI covered.

Continuous assurance

Monitoring and periodic retests.

Scope

What is in, what is not

Included

Accuracy testing
Bias and safety review
Model cards
Monitoring setup

Handled separately

Rebuilding the model
Formal certification

Bilytica frameworks behind this service

Our own methods, not a generic playbook

TRUST

AI Assurance Framework

One control set that answers NIST, ISO 42001, the EU AI Act and the Saudi regulators at once.

See the framework DELIVER

AI Delivery Framework

A gated path from idea to live service, with evidence at every step.

See the framework

All Bilytica frameworks

Where this sits

Foundation

Intelligence

Agents

Our point of view

What we are seeing in the market

Observations from the engagements and tenders we work on across Saudi Arabia and the wider Gulf.

Nobody believes an AI until it is measured

Demonstrations convince a room once. Boards, regulators and customers now ask for a number: how often is it right, how often is it grounded in your own data, and what happens on the cases where it is not.

Evaluation is now a deliverable, not a phase

The systems that survive contact with production ship with a test set that grows every week, real questions, expected answers, known traps, run automatically on every prompt, model or retrieval change.

Arabic changes the result

An assistant that scores well in English routinely degrades in Arabic, dialect, transliteration, mixed script and numerals. Assurance that never tested Arabic has not tested the system your users will actually meet.

The work

What this practice delivers

Evaluation harness

A living test set per use case in Arabic and English, scored for correctness, grounding, refusal behaviour, tone and latency, wired into the release pipeline so no change ships unmeasured.

Grounding and hallucination scoring

Every answer traced back to the source that justified it, with an unsupported-claim rate reported per release rather than discovered by a customer.

Bias, safety and red-teaming

Structured adversarial testing, prompt injection, data exfiltration, unsafe instructions, demographic parity, repeated on a schedule, not once at launch.

Evidence packs and standards mapping

Results mapped to ISO 42001 AI management controls NCA security expectations and PDPL obligations, assembled as a readable pack.

How the work is structured

An assurance loop that gets stricter every release

Define

Use-case risk tier
Acceptance thresholds

Build test set

Real questions, AR + EN
Known traps

Score

Correctness & grounding
Bias & safety

Gate the release

Pass or block
Rollback path

Evidence

Standards mapping
Audit-ready pack

Production failures return to the test set, so the same mistake cannot ship twice

Selected work

Work we have delivered

Client identities are withheld. The situations, the build and the change afterwards are as they happened.

Assistant evaluation

A Gulf bank

The situation: A customer-facing assistant passed internal review but produced confident answers about products that had been withdrawn.
What we built: An evaluation harness with a versioned product corpus, grounding checks on every answer and a release gate on unsupported-claim rate.
What changed: Unsupported answers became a tracked number that fell release over release instead of an anecdote.

Arabic safety testing

A government entity

The situation: An Arabic service assistant behaved differently when users mixed dialect and transliterated terms.
What we built: A dialect-aware evaluation set, refusal and escalation checks, and adversarial prompts run before every model change.
What changed: The service passed review with documented Arabic results rather than an English proxy.

Where we are strongest

We are strongest where an AI system has to be defended in writing to someone who was not in the room.

Measurement before opinion

We start by making the system measurable, because every argument about quality ends the moment there is a number both sides trust.

Arabic tested deliberately

Dialect, transliteration and mixed-script inputs are part of the test set from the first run, not added after a complaint.

Independent of who built it

We assure systems we did not build, including vendor products, and we report the result as it is.

Evidence as a by-product

The harness emits the pack. Preparing for an audit becomes retrieval rather than a project of its own.

Straight answers

Questions we are asked most

We already have a governance policy. Why assurance?

Policy states the intent. Assurance produces the measurement that shows the intent is being met, on this release, in this language.

Can you assure a system built by another vendor?

Yes. We test through the interface your users see, so we do not need their source code to report grounding, safety and Arabic behaviour.

How is this different from ordinary QA?

QA asks whether the software works. Assurance asks whether the answers are true, sourced, safe and fair, and reports how often they are not.

Clients

Organisations we work with

Across banking, healthcare, government, retail, energy and telecom.

FAQ

Frequently asked questions

What does an AI assurance engagement produce?

A versioned evaluation harness in your environment, scores per release for correctness, grounding, bias and safety, a red-team report, and an evidence pack mapped to ISO 42001 and PDPL.

Do you cover prompt injection and data exfiltration?

Yes. Both are standard red-team categories, tested against the retrieval layer and any tools or actions the agent can call.

Can assurance run continuously?

Yes, the harness runs on every change and on a schedule, and production failures are folded back into the test set automatically.