Service
AI evaluation & assurance
Measure the system before it reaches production
Siyada Tech builds evaluation programmes for enterprise AI systems. We create task-realistic Arabic and English test sets, define scoring rubrics with the domain owner, run them through Qiyas, and turn the results into release gates and a documented record for audit and governance committees.
Last reviewed: — Siyada Tech engineering
What it is
An assurance practice: evaluation dataset design, rubric definition, baseline measurement, regression tracking across releases, and reporting suitable for risk and governance review.
The problem it solves
AI systems are approved on demonstrations. Without a measured baseline, nobody can tell whether a change improved the system, and governance committees have nothing to review except a vendor's assertion.
Who it is for
- Organisations moving an AI pilot toward production
- Risk, internal audit and model governance functions
- Teams inheriting an AI system with no measurement history
How it works
- 01Collect real tasks from the operational team, in Arabic and English.
- 02Define rubrics: task success, groundedness, citation validity, safety and tone.
- 03Establish a baseline measurement on the current system.
- 04Wire evaluation into CI so every change is scored automatically.
- 05Set release thresholds and report per release to the governance owner.
Deployment
- Qiyas deployed in-tenant alongside the evaluated system
- CI integration for automated runs
- Reports exportable for audit and board review
Security & compliance
- Evaluation data, including any personal data, stays inside the tenant
- PDPL-aligned test set construction; personal data minimised or synthetic where possible
- Immutable run history for audit reconstruction
Evidence
We publish a metric only with its definition, method, sample size and evaluation date. Anything not yet measured to that standard is marked below rather than claimed.
| Metric | Definition | Method | Sample size | Evaluated | Limitations |
|---|---|---|---|---|---|
| Time from kickoff to production | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] |
| Task success rate in production | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] |
| Human-review escalation rate | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] | [EVIDENCE REQUIRED] |
Limitations & what it does not do
- 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.