A leading European technology company set out to develop a conversational AI capable of engaging users in multiple languages across its customer service and B2B support platforms. While their English models worked well, the system struggled with regional European languages, dialects, and accents, which limited its adoption across diverse markets.

To overcome this challenge, the company partnered with GetAnnotator, a trusted provider of remote data annotation services to companies, industries, and universities worldwide.

The Challenge

The European company faced several hurdles while preparing training datasets:

• Lack of regional speech datasets, especially in languages like Catalan, Polish, and Dutch.

• Low-quality transcriptions and inconsistent intent tagging in available data.

• Poor AI performance when handling accents, code-switching, and background noise.

Without addressing these issues, the AI system risked losing user trust and could not scale across Europe’s linguistically diverse customer base.

How GetAnnotator Helped

GetAnnotator deployed its remote workforce of expert annotators to deliver accurate, scalable, and multilingual speech datasets.

1. Curating Diverse Speech Data

• Gathered real-world audio samples across 15+ European and global languages.

• Ensured inclusion of various accents, tones, and noisy environments for real-world reliability.

2. High-Quality Transcription & Intent Tagging

• Delivered speech transcriptions with 97%+ accuracy.

• Labeled each audio segment with clear intent categories such as “billing inquiry,” “technical support,” and “account update”.

3. Scalable Remote Annotation Workforce

• Leveraged GetAnnotator’s distributed annotator network across multiple regions.

• Allowed the European company to scale data labeling quickly without hiring in-house teams.

4. Multi-Layer Quality Assurance

• Applied three-stage review cycles to minimize errors.

• Created feedback-driven consistency checks to maintain data reliability.

Impact

Here’s a snapshot of the measurable impact GetAnnotator delivered:

Metric	Before GetAnnotator	After GetAnnotator	Improvement
Intent Recognition Accuracy	78%	95%	+17%
Dataset Preparation Time	10 weeks	6 weeks	40% faster
Annotation Accuracy (Transcriptions)	~85%	97%+	+12%
User Satisfaction Scores (Europe)	3.2/5	4.6/5	+44%

Key Statistics:

• 95% intent recognition across 15+ European languages.

• 40% faster dataset preparation compared to initial estimates.

• 12% boost in annotation accuracy through multi-layer QA.

• 44% increase in end-user satisfaction post-deployment.

Why GetAnnotator Made the Difference

The European company credited GetAnnotator’s approach for three main reasons:

• Global Reach, Local Expertise: Access to annotators fluent in regional European languages.

• Scalable Remote Model: Ability to ramp up or scale down resources quickly.

• High Accuracy Standards: Rigorous QA ensured datasets were reliable and production-ready.

Conclusion

By partnering with GetAnnotator, the European company successfully transformed its conversational AI into a multilingual system that could serve customers across different markets. This case study demonstrates how GetAnnotator’s remote data annotation services enable global enterprises from European tech leaders to B2B industries and universities to overcome dataset challenges and accelerate AI development.