Model Evaluation & Safety
AI Safety & Bias Auditing in Kenya.
Evidence-based safety and bias testing for AI models.
Systematic testing that finds where a model behaves unsafely or unfairly for particular people or prompts, even when its average accuracy looks good.
- Red teaming
- Fairness rubric
- Reproducible findings
100%
Of findings reproducible
- Red teaming, bias, policy conformance
- Tests
- Reproducible, severity-rated findings
- Output
- Diverse, trained and calibrated
- Team
- Pre-launch sprints or ongoing monitoring
- Timing
Overview
Affordable AI safety and bias auditing for companies in Canada, Australia, Saudi Arabia and South Africa
- 01
Auditors probe your model with adversarial and demographic-sensitive prompts, using a defined harm taxonomy and fairness rubric.
- 02
They look for harmful content, stereotyping, unequal quality across groups, and failures to refuse or to help.
- 03
Every finding includes the prompt, the output, a harm category and a severity rating, so your team can reproduce, fix and re-test it.
- 04
A diverse team catches blind spots, especially in African and other non-Western contexts.
- 05
Engagements range from pre-launch assessments and red-team sprints to ongoing monitoring of production models.
At a glance
Targets are agreed per client and written into your SLA.
- Of findings reproducible
- 100%
- Severity scale on every issue
- 4 levels
- Shortest red-team sprint
- 1 week
- Re-test after your fixes
- Included
What's included
What's included in our ai safety & bias auditing service.
Take one piece or all of it. Every item runs to documented procedures with an owner, a target and weekly reporting.
- 01
Red teaming
Creative, structured attempts to make the model misbehave.
- 02
Bias evaluation
Comparing behaviour and quality across groups.
- 03
Policy testing
Checking outputs against your safety policies.
- 04
Jailbreak testing
Known and new ways around your safeguards.
- 05
Pre-launch risk review
A structured assessment before release.
- 06
Fix verification
Re-testing issues after your changes.
How it fits together
Test, document, fix, re-test.
Every issue is captured with evidence and severity, handed to your team to fix, and re-tested before sign-off, so you can show exactly what changed.
How it works
How ai safety & bias auditing with Maxed BPO works.
A supervised path from first call to a fully managed team, usually live within 2 to 3 weeks.
- 1
Scope
We agree the risks, policies, groups and severity levels.
- 2
Probe
Auditors test the model systematically.
- 3
Document
Findings are logged with evidence and severity.
- 4
Re-test
Fixes are checked before sign-off.
Who it's for
Who uses our ai safety & bias auditing service.
- Companies launching AI products
- LLM developers and fine-tuners
- Banks and insurers using AI for decisions
- Public sector AI projects
- Teams preparing for AI regulation
Platforms
Tools and platforms we work in.
Your team works inside the systems you already use. Don't see yours? We learn custom tools too.
- Your model API
- Custom prompt sets
- Google Sheets
- Airtable
- Jira
- Label Studio
- Python notebooks
- Slack
- Confluence
- Notion
FAQ
AI Safety & Bias Auditing: your questions.
Something else on your mind? Our team replies within one working day.
Speak to a specialist
Tell us your volumes and goals. We reply within one working day with a suggested team and price.
Get a free quoteWhat makes your team suited to bias testing?
A broad mix of backgrounds and languages, plus training on a clear harm taxonomy.
Do you write the test prompts?
Yes, and they can be combined with your own prompt sets.
Is this a one-off or ongoing?
Either. Many clients run a pre-launch sprint, then move to ongoing monitoring.
How are auditors protected?
Exposure limits, rotation and wellbeing support, as with our moderation teams.
Related AI services
Often paired with ai safety & bias auditing.
Get a free AI safety audit quote
A no-obligation consultation to design your team, hours and price. Most quotes are back within one working day.