Find the gap.
Close the gap.
Per-slice accuracy and disparity analysis across demographic, intersectional and contextual slices — plus counterfactual probes and concrete mitigation guidance.
Built for production, not just demos.
- Per-slice accuracy + disparity reporting (demographic parity, equalised odds)
- Intersectional slice analysis (e.g. age × gender × dialect)
- Counterfactual probes (swap protected attribute, measure delta)
- Stereotype + association probes (WEAT, SEAT)
- Toxic-output rate by target group
- Mitigation guidance: data, reward, decoding, deployment
- Multilingual + culturally-adapted slices
- Continuous drift monitoring in production
How a typical engagement runs.
Scope
Identify protected attributes, slices and stakeholders + your fairness target.
Sample
Curate balanced + adversarial test sets; counterfactual pairs across slices.
Measure
Per-slice accuracy + disparity metrics; intersectional gaps surfaced.
Mitigate
Concrete guidance: data balancing, reward shaping, decoding, post-filter.
Re-measure
Pre/post mitigation diff with statistical significance.
What you get in your bucket.
Questions, answered.
Which fairness metrics do you support?
Demographic parity, equalised odds, equal opportunity, counterfactual fairness, calibration parity, and several model-specific metrics. We co-pick the right ones with your team.
Can you handle intersectional analysis?
Yes — we analyse up to 4-way intersections (e.g. age × gender × dialect × accent) with appropriate statistical controls.
Does mitigation guidance actually work?
Our typical engagement closes 60-80% of measured disparity within one mitigation cycle. We re-measure to verify.
Is this only for English?
No — we run bias probes in 60+ languages with culturally-adapted slices (caste, region, dialect for India; tribe/region for Africa; etc).
Ready to build
AI you can trust?
Talk to a solutions architect — get a pilot scoped in 48 hours.