Client Experiences
Feedback from organizations implementing our voice technology solutions
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Honest feedback from Malaysian organizations using our services
Ahmad Hassan
Operations Manager, Kuala Lumpur
The voice analytics system has been particularly helpful for reviewing customer service interactions. We appreciated that the team was upfront about what accuracy to expect given our phone line quality, rather than making exaggerated claims. The implementation took the timeline they quoted, and the support afterward has been responsive.
January 28, 2026
Mei Ling Soh
IT Director, Petaling Jaya
We needed speech recognition customized for technical terminology in our domain. The assessment process helped us understand what was realistic before committing to development. The actual recognition performs well for our use case, though we occasionally need to retrain when new product names are introduced. Having a local team we can meet with face-to-face has been valuable.
February 3, 2026
Raj Kumar
Customer Experience Lead, Johor Bahru
The voice interface assessment was worth doing before we invested in a full implementation. It identified some language patterns in our customer base we hadn't fully considered and helped us set appropriate expectations with stakeholders about what the technology could and couldn't do. The recommendations were practical and grounded in our actual requirements.
January 19, 2026
Nurul Izzah
Compliance Officer, Kuala Lumpur
Data privacy was a major concern for us, and Vocalis provided detailed documentation of how voice data would be handled in compliance with Malaysian regulations. The on-premises deployment option was important for our security requirements. The team understood our compliance needs and worked within those constraints rather than trying to push us toward a cloud-only solution.
February 7, 2026
Tan Chee Wai
Product Manager, Penang
We appreciate the technical depth of the team. They could explain the actual mechanics of how the speech recognition works, which helped our developers integrate it properly. The code-switching between Bahasa and English is handled better than other solutions we tested. There are still some edge cases we're working through, but overall performance meets our needs.
January 15, 2026
Sarah Binti Aziz
Training Coordinator, Selangor
The voice analytics dashboard has made it much easier to identify training needs across our customer service team. We can now systematically review call quality rather than relying on random sampling. The sentiment analysis is not perfect but gives us useful signals about which interactions warrant closer review. The team provided good training on interpreting the analytics appropriately.
January 23, 2026
Implementation Outcomes
Detailed examples of how organizations have applied our solutions
CHALLENGE
A financial services provider needed to analyze customer service calls for compliance monitoring but found manual review time-consuming and inconsistent. They wanted systematic coverage across all interactions while maintaining data privacy standards.
SOLUTION
We implemented a voice analytics system configured for their compliance requirements. The system processes call recordings through speech recognition and applies natural language analysis to flag potential compliance issues. Access controls ensure only authorized personnel can review flagged interactions.
RESULTS
The organization now reviews 100% of interactions rather than 5% sample previously used. Compliance team can focus attention on flagged items requiring human judgment. Processing time per interaction reduced from 15 minutes of manual review to automated screening plus selective detailed review.
Timeline: 5 weeks from initial consultation to production deployment
The system handles the mechanical screening work, freeing our compliance team to focus on the nuanced situations that genuinely require expertise.
CHALLENGE
A healthcare application wanted to add voice input for patient symptom reporting, but existing speech recognition struggled with medical terminology and the mix of English and Bahasa Malaysia used by patients when describing symptoms.
SOLUTION
We developed a custom speech recognition model trained on symptom descriptions in both languages. The training included common Malaysian pronunciation patterns for medical terms and code-switching between Bahasa and English. Noise suppression was tuned for typical home environments.
RESULTS
Voice input accuracy for symptom reporting reached 87% on clean audio and 78% in noisy conditions, compared to 62% with generic speech recognition. Patient adoption of voice input feature increased as reliability improved. System correctly handles code-switching in 84% of mixed-language utterances.
Timeline: 8 weeks including data collection and iterative testing
The difference in recognition quality between generic and customized models was substantial enough that voice became a viable input method for our application.
CHALLENGE
An e-commerce platform was considering voice search capability but needed to understand feasibility given their product catalog structure and the linguistic diversity of their Malaysian customer base before committing development resources.
SOLUTION
We conducted a voice interface assessment analyzing their product taxonomy, typical search patterns, and sample voice queries from user research. The assessment evaluated technical approaches and provided accuracy projections based on catalog complexity and expected query types.
RESULTS
Assessment identified that voice search would work well for broad category navigation but face challenges with specific product attributes. Recommendations included starting with category-level voice navigation while maintaining text search for detailed queries. Cost and timeline estimates allowed informed prioritization against other features.
Timeline: 2.5 weeks from kickoff to final recommendations
The assessment prevented us from building something that wouldn't have worked well, and gave us a realistic roadmap for voice features that would actually provide value.
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