Real Experiences from Engineering Teams
Organizations across Southeast Asia share their experiences working with our team on ML operations, conversational systems, and performance optimization projects.
Back to HomeWhat Our Clients Say
Feedback from technical leaders and engineering managers who have worked with our team on production ML systems.
Their MLOps engagement helped us transition three fraud detection models from manual deployment to automated pipelines. The monitoring system they implemented caught a data quality issue in the first week that would have caused serious problems. Most valuable was how they worked alongside our team rather than just handing over documentation.
The conversational AI project took longer than initially estimated, mainly because we kept requesting changes to the dialogue flows based on user testing. However, the final system feels natural and handles edge cases well. Their iterative approach to testing and refinement made a big difference in the quality of interactions.
Performance tuning engagement reduced our model serving costs by 43% while improving p95 latency from 280ms to 95ms. They identified bottlenecks in our preprocessing pipeline that we had overlooked. The detailed benchmarking report gave us clear evidence to justify infrastructure changes to management.
We needed help establishing proper ML infrastructure and the team delivered exactly what was promised. The documentation they provided is comprehensive enough that our junior engineers can follow deployment procedures independently now. They were also honest about which features we didn't actually need, saving us development time.
Their conversational AI work on our customer service chatbot improved completion rates significantly. Users can actually get their questions answered now instead of getting transferred to agents. The dialogue design workshop sessions helped our team understand how to structure conversations more effectively for future improvements.
Appreciated that they focused on making our existing models work reliably rather than suggesting we rebuild everything from scratch. The monitoring dashboard they set up has been essential for catching issues early. Their engineers were patient in explaining their reasoning and actively involved our team in technical decisions throughout the project.
Detailed Success Stories
In-depth accounts of engagements showing specific challenges, approaches, and measurable outcomes.
Challenge
Regional e-commerce platform had three recommendation models running in production with no version control, manual deployment process taking 4-6 hours, and no systematic monitoring. Model updates often broke production, requiring emergency rollbacks.
Solution
Implemented MLOps infrastructure including model registry, automated testing pipeline, and gradual rollout system. Established monitoring for model performance metrics and data drift. Documented procedures for model updates and troubleshooting common issues.
Results
Deployment time reduced to 20 minutes with zero production incidents over three months. Team can now release model updates weekly instead of monthly. Monitoring caught two data quality issues before they affected users.
"The infrastructure changes have made model updates straightforward enough that junior engineers can handle them following the documented procedures." - Engineering Manager
Challenge
Healthcare technology company needed patient inquiry chatbot but previous attempt resulted in frustrated users and high transfer rates to human agents. System couldn't handle variations in how patients phrased questions or provide contextually appropriate responses.
Solution
Redesigned conversation flows based on user journey analysis. Implemented improved intent classification with better handling of ambiguous queries. Created response templates covering common edge cases. Conducted iterative testing with actual patients and refined based on feedback.
Results
Inquiry completion rate increased from 31% to 68%. Transfer to agent rate dropped from 54% to 22%. User satisfaction scores improved from 2.3 to 4.1 out of 5. System now handles appointment scheduling, medication questions, and billing inquiries effectively.
"The difference from our first chatbot is night and day. Patients can actually get their questions answered, and the system handles unexpected phrasing much better." - Product Lead
Challenge
Financial services company's fraud detection system had acceptable accuracy but p95 latency of 850ms was causing transaction delays. Monthly infrastructure costs were climbing as transaction volume increased. Management questioning whether system could scale economically.
Solution
Profiled inference pipeline to identify bottlenecks. Optimized feature preprocessing through caching and batch operations. Implemented model quantization reducing memory footprint. Adjusted serving infrastructure configuration. Established performance monitoring to track improvements.
Results
P95 latency reduced from 850ms to 180ms. Monthly infrastructure costs decreased by 38% despite 20% growth in transaction volume. System now comfortably handles peak loads. Accuracy maintained within 0.2% of original performance.
"The performance improvements gave us confidence to expand the system to additional transaction types we had been hesitant about due to latency concerns." - Platform Architect
By the Numbers
Contact Information
91/2 Wireless Road, Lumpini, Pathum Wan
Bangkok 10330, Thailand
Monday - Friday: 9:00 AM - 6:00 PM
Saturday: 10:00 AM - 2:00 PM
Sunday: Closed
Start Your Own Success Story
If you're looking for practical engineering support for your ML systems, we'd be glad to discuss how we might assist your team.