Enterprise & Advisory AI — ELOQUENCE
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AI advice that's fair
to everyone.

ELOQUENCE builds advisory AI that doesn't just avoid bias — it actively detects and eliminates it. Measurable, auditable, and evaluated across 24+ EU languages and cultures.

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50%
Bias reduction target across all pilots
24+
EU languages evaluated for bias
4
Bias types detected: gender, culture, ethnicity, religion
AI Act
EU AI Act compliant for high-risk advisory systems
The challenge

Biased AI advice causes
real harm.

When AI systems recommend careers, evaluate candidates, or provide guidance — they carry the biases of their training data. A system that steers women away from STEM, or disadvantages candidates from certain ethnic backgrounds, causes measurable, documented harm. And it exposes your organisation to serious legal and reputational risk.

⚖️
Hidden bias in training dataLLMs trained on historical data absorb historical biases — and reproduce them at scale without anyone noticing.
🌐
Cultural context ignoredA response appropriate in one EU country may be biased or offensive in another. Most AI systems don't account for this.
📋
No audit trailWhen a biased recommendation is challenged, there's no way to prove the system was tested or that steps were taken to mitigate it.
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EU AI Act exposureAdvisory AI in employment, education, and career guidance is classified as high-risk under the EU AI Act. Non-compliance carries significant penalties.
The ELOQUENCE solution
Active bias detection
Synthetic persona datasets surface hidden bias before deployment
We generate diverse synthetic user profiles and evaluate system responses across gender, ethnicity, religion, and cultural context.
Prompt engineering controls
Bias mitigation built into the model's response generation
Not just filtered after the fact — bias-aware prompting changes how the model generates responses from the start.
Multilingual evaluation
Bias tested across 24+ EU languages and cultural contexts
A response can be unbiased in English but carry cultural assumptions when translated. We test both.
Audit-ready reporting
Measurable benchmarks and documented test results
Full audit trail of bias evaluation methodology — ready for EU AI Act compliance documentation.
Bias types detected

Four dimensions.
Zero tolerance.

ELOQUENCE evaluates advisory AI across four distinct bias dimensions — because fairness isn't a single metric.

♀♂
Gender bias
Does the system recommend different careers, salaries, or opportunities based on perceived gender? Tested with matched synthetic personas.
🌍
Cultural bias
Do responses reflect assumptions specific to one cultural context? Evaluated across EU member state cultural norms.
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Ethnic bias
Are recommendations consistent regardless of inferred ethnic background? Tested via name-based and context-based persona variations.
🕌
Religious bias
Does the system treat candidates differently based on religious context or practice? Evaluated across EU's major faith communities.
How it works

Bias you can measure.
Results you can prove.

ELOQUENCE's bias detection methodology is systematic, documented, and repeatable — not a one-time check before launch.

01
Synthetic persona generationWe create diverse user profiles varying across gender, ethnicity, religion, and cultural background — matched pairs to isolate each variable.
02
Identical query testingThe same advisory query is submitted by each persona. Response differences are measured and scored.
03
Multilingual evaluationTests run in all target languages — because bias can appear in translation even when the English version is clean.
04
Mitigation appliedPrompt engineering and model adjustments reduce identified bias. The process repeats until targets are met.
05
Benchmark documentedResults are recorded with full methodology — ready for EU AI Act compliance audits.
Evaluated across 24+ EU languages
EnglishGermanFrenchSpanishItalianPolishDutchPortugueseRomanianCzechHungarianSlovakBulgarianCroatianDanishFinnishGreekSwedishLithuanianLatvianEstonianSlovenianIrishMaltese

All 24 official EU languages covered. Bias evaluation ensures consistent fairness standards across every market you operate in.

Outcomes

Measurable targets.
Documented results.

ELOQUENCE's bias detection pilot was led by CNR — the National Research Council of Italy — focused on career counselling and university recommendation AI.

50%
Bias reduction target
Measured reduction in biased outputs across all four dimensions — gender, culture, ethnicity, religion — compared to baseline.
Project-wide target (KR18)
24+
EU languages evaluated
Bias testing conducted in all official EU languages — ensuring consistent fairness standards regardless of the user's language.
Source: EuroLLM-9B evaluation
Audit
Ready documentation
Full methodology, test results, and benchmark scores documented in a format ready for EU AI Act high-risk system compliance review.
EU AI Act Article 9 alignment

Build advisory AI you can
stand behind.

Talk to us about evaluating your existing AI system for bias — or deploying a new advisory AI that meets EU AI Act standards from day one.

Request an enterprise demo →
Bias audit of your existing system — we'll run our evaluation framework on your current AI.
EU AI Act compliance review — map your system against high-risk requirements.
Multilingual evaluation — tested in whichever EU markets matter most to you.
48-hour response — from the research team that developed the methodology.