Client Experiences
Hear from organisations we've worked with about their AI journey and the outcomes achieved through our collaborative approach.
Return HomeWhat Our Clients Say
Authentic feedback from healthcare providers, enterprises, and organisations across Singapore who've partnered with us for AI consulting.
Dr. Linda Chen
Medical Director, Singapore
Working with Aethonix on our clinical decision support system was refreshingly different from previous AI vendor experiences. They took time to understand our workflows before proposing solutions, and their respect for patient safety considerations was evident throughout. The system they helped us develop has genuinely improved our diagnostic confidence in complex cases.
January 28, 2026
Rajesh Tandon
CTO, Singapore
The AI maturity assessment gave us an honest picture of where we stood, which was more valuable than the optimistic assessments we'd received elsewhere. Their staged progression plan has guided our AI investments over the past year, and we've avoided several costly missteps by following their recommendations about foundational work before pursuing advanced capabilities.
February 3, 2026
Sarah Wong
VP Operations, Singapore
Aethonix helped us adapt a foundation model to our specialised domain with remarkably limited data. The transfer learning approach they designed was both technically sound and practical to implement. While the timeline was slightly longer than initially estimated, the quality of the final model justified the additional care taken in validation.
January 19, 2026
Kevin Lim
Head of Innovation, Singapore
What impressed me most was their willingness to tell us when AI wasn't the answer. They redirected one of our proposed projects towards simpler automation that better suited the actual problem. That honesty saved us significant resources and built trust that their recommendations were genuinely in our interest.
February 11, 2026
Alicia Ng
Clinical Manager, Singapore
The team's understanding of healthcare regulatory requirements was exceptional. They navigated compliance considerations without treating them as obstacles, integrating privacy and safety protocols naturally into the solution design. Our clinical staff felt genuinely consulted throughout the process rather than having AI imposed on their workflows.
January 25, 2026
Marcus Tan
Data Lead, Singapore
The documentation they provided was comprehensive enough that our team could maintain and extend the solution independently. They explained technical concepts clearly without condescension, which helped build our internal AI capability. I appreciated their patience with our questions and their focus on knowledge transfer rather than creating dependency.
February 8, 2026
Success Stories
Detailed case studies showing how our collaborative approach has helped organisations achieve their AI objectives.
Healthcare Provider Implements Clinical Decision Support
Challenge
A mid-sized healthcare facility wanted to improve diagnostic accuracy in emergency department cases involving complex presentations. They had attempted AI implementation previously but faced issues with clinical staff adoption and concerns about patient safety integration.
Solution
We conducted thorough workflow analysis with emergency department physicians, designed a decision support system that enhanced rather than replaced clinical judgment, and implemented rigorous safety protocols. The solution integrated seamlessly with existing EMR systems while respecting clinical autonomy.
Results
Within six months, the system contributed to 18% improvement in diagnostic confidence scores on complex cases. Clinical staff adoption reached 73% (well above typical rates for clinical AI). Most importantly, no safety incidents were attributed to the system during validation period.
"The difference was Aethonix's genuine respect for clinical judgment. They positioned AI as an assistant to our physicians, not a replacement, which made all the difference in adoption."
Dr. Patricia Lim, Emergency Department Director
Timeline: 12 weeks from discovery to deployment | Engagement Type: Healthcare AI Applications
Enterprise Adapts Foundation Model for Specialised Domain
Challenge
A Singapore-based enterprise needed natural language processing capabilities for a highly specialised technical domain. Their dataset was too limited for training from scratch, and generic models performed poorly on domain-specific terminology.
Solution
We evaluated multiple foundation models for domain similarity, designed a fine-tuning approach that worked within their data constraints, and implemented rigorous validation to ensure reliable performance. The transfer learning methodology allowed effective adaptation with approximately 30% of the data typically required.
Results
The adapted model achieved 87% accuracy on domain-specific tasks, compared to 42% from generic models. Implementation took 6 weeks versus the 4-6 months estimated for training from scratch. The client's team now maintains and extends the model independently using our documented methodology.
"We were skeptical about achieving good performance with our limited dataset, but Aethonix's transfer learning approach proved highly effective. More importantly, they taught us how to continue this work ourselves."
David Chua, Technical Lead
Timeline: 6 weeks from model selection to deployment | Engagement Type: Transfer Learning Solutions
Organisation Develops Clear AI Progression Plan
Challenge
A growing organisation recognised AI's potential value but lacked clarity on where to begin. Previous attempts had stalled due to unclear priorities, unrealistic expectations, and insufficient foundational capabilities. Leadership wanted an honest assessment before further investment.
Solution
Our AI maturity assessment evaluated capabilities across seven dimensions, conducted stakeholder interviews at all levels, and assessed infrastructure readiness. We provided a structured scorecard showing current state, identified high-impact gaps, and designed a staged progression plan respecting their budget and timeline constraints.
Results
The organisation gained clarity on foundational work needed before pursuing advanced AI. They invested in data infrastructure improvements first, as recommended, which enabled successful AI pilots nine months later. The staged approach prevented costly missteps and built genuine internal capability.
"The assessment was refreshingly honest about our readiness. Rather than selling us on advanced AI we weren't prepared for, Aethonix gave us a realistic roadmap that's guided our AI strategy effectively."
Jennifer Koh, Chief Strategy Officer
Timeline: 4 weeks from kickoff to final recommendations | Engagement Type: AI Maturity Assessment
Trust Indicators
Metrics that demonstrate our commitment to quality outcomes and client satisfaction.
47
Organisations Served Since 2021
4.8/5
Average Client Satisfaction Rating
68%
Clients Return for Additional Projects
92%
Projects Completed On Time or Early
Contact Information
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+65 6193 4527
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16 Collyer Quay, #21-00
Income at Raffles
Singapore 049318
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Saturday: 10:00 AM - 2:00 PM
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