Capability 05 · Assurance
Measurement before release
AI evaluation & assurance
Know whether a change made the system better before your people find out.
In one paragraph
Siyada Tech builds evaluation programmes for AI systems. We create realistic Arabic and English test sets from real tasks, agree scoring rubrics with the domain owner, run them through an evaluation harness, and turn the results into release gates and a documented record that risk and governance committees can review.
- Rubrics and baselines
- Regression tracking
- Release gates
01What it is
An assurance practice: evaluation dataset design, rubric definition, baseline measurement, regression tracking across releases, and reporting suitable for risk and governance review. It applies to systems we build and to systems you bought.
02Who it is for
- Organisations moving an AI pilot towards production
- Risk, internal audit and model-governance functions
- Teams that inherited an AI system with no measurement history
03The problem
AI systems are approved on demonstrations. Without a measured baseline, nobody can tell whether a change to a prompt, model or index improved the system or quietly broke it, and governance committees have nothing to review except a vendor's word.
04How it works
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01
Collect real tasks from the operational team, in Arabic and English.
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02
Agree rubrics with the domain owner
Task success, groundedness, citation validity, safety and tone.
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03
Establish a baseline on the system as it runs today.
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04
Wire evaluation into the release pipeline, so every change is scored automatically.
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05
Set release thresholds and report each release to the governance owner.
05Where it runs
- The evaluation harness deployed in the client's tenant, next to the system it evaluates
- Runs automatically on every change through the release pipeline
- Reports exported for audit committees and boards
06Security and data
- Evaluation data, including any personal data, stays in the tenant
- Test sets built with personal data minimised, or synthetic where possible
- Run history is versioned and immutable, for audit reconstruction
07Evidence
What we measure here. Results are published once each one has a stated method, sample size and evaluation date. How we publish evidence
- Regressions caught before release
- Evaluation coverage of production task types
- Time from change to release decision
08What it is not
- Evaluation covers what the test set represents. New failure modes need new tests.
- Rubric scoring involves human judgement and needs periodic recalibration.
- Assurance reduces risk. It does not certify safety or guarantee outcomes.
?Asked often
Questions
Why does an AI system need evaluation?
Because changes to prompts, models and indexes alter behaviour silently. Without a baseline you cannot tell an improvement from a regression.
What do you measure?
Task success, groundedness, citation validity, safety and tone, scored separately for Arabic and English.
Can you evaluate a system we bought?
Yes. Any system reachable through an API can be evaluated, including third-party products.
What does a governance committee receive?
A versioned report for each release: what changed, what regressed, and against which thresholds.
How do we start?
We collect real tasks from your team and produce a baseline report on the system you run today.
Get a baseline on the system you run today
We measure it against your real tasks and show you where it actually stands.
Last reviewed · Siyada Tech engineering