Building Capable AI Systems Since 2019
We help organizations in Thailand and Southeast Asia implement AI with technical rigor and practical focus.
Back to HomeWho We Are
Aether Fold was established in 2019 by engineers who saw a gap between academic AI research and operational deployment. Organizations wanted to adopt machine learning but struggled with the practical aspects — data preparation, model selection, production infrastructure, and ongoing maintenance.
Based in Hat Yai, we work primarily with mid-sized organizations across manufacturing, logistics, retail, and professional services. Our projects range from computer vision systems that inspect products on assembly lines to strategic workshops that help leadership teams understand where AI can genuinely add value.
Our approach emphasizes knowledge transfer. We don't believe in delivering black-box solutions that leave clients dependent on external consultants. Instead, we work alongside your technical teams, explaining decisions, documenting processes, and building internal capability. When an engagement ends, you should understand not just what was built, but why and how to maintain it.
We maintain a small team intentionally. This allows us to be selective about projects and ensure every engagement receives appropriate technical attention. Our engineers have production experience with TensorFlow, PyTorch, and modern MLOps tooling. We stay current with research but filter for practical applicability rather than chasing every new technique.
Our Team
Engineers and strategists with production AI experience
Anuwat Siriwan
Technical Director
Leads computer vision implementations and model deployment architecture. Previously built production ML systems for logistics optimization in Bangkok.
Pornpimol Thanaporn
Strategy Consultant
Facilitates AI strategy workshops and organizational readiness assessments. Background in digital transformation consulting across Southeast Asia.
Kittipong Chaiyasit
MLOps Engineer
Specializes in model monitoring infrastructure and production deployment pipelines. Experience with Kubernetes, Docker, and cloud platforms.
How We Work
Principles and practices that guide our engagements
Data-Driven Validation
Every model we build includes comprehensive performance metrics, error analysis, and validation against realistic test scenarios. We document limitations clearly and honestly.
Production Readiness
Our deliverables include monitoring dashboards, failure handling, and operational runbooks. We test deployment procedures in staging environments before production release.
Knowledge Transfer
We conduct technical handoff sessions, provide detailed documentation, and remain available for follow-up questions. Your team should be able to maintain and extend the work.
Data Security
All work occurs within your infrastructure or designated environments. We follow your security protocols and sign appropriate confidentiality agreements before accessing any data.
Regular Communication
Weekly progress updates, documented decisions, and prompt response to questions. We adapt to your preferred communication channels and meeting schedules.
Honest Assessment
If AI isn't appropriate for your use case, we'll tell you. If a simpler approach would work better, we'll recommend it. We prioritize long-term relationships over individual project revenue.
Technical Capabilities
Our engineering team maintains active expertise across computer vision architectures, including convolutional networks for image classification, object detection frameworks like YOLO and Faster R-CNN, and semantic segmentation approaches for pixel-level analysis. We've deployed these in quality inspection, spatial analysis, and visual search applications.
For production deployments, we work with containerization using Docker, orchestration via Kubernetes, and monitoring tools including Prometheus and Grafana. Our MLOps practice covers continuous integration pipelines for model retraining, A/B testing infrastructure, and automated performance degradation detection.
We adapt to your existing technology stack rather than imposing specific requirements. Most projects involve Python with PyTorch or TensorFlow, but we've integrated with Java backends, Node.js services, and legacy systems where needed. Cloud platform experience includes AWS, Google Cloud, and Azure, plus on-premise deployments for organizations with data sovereignty requirements.
Strategy engagements draw on our understanding of organizational change management, technical due diligence processes, and AI maturity assessment frameworks. We help leadership teams develop realistic roadmaps that account for data infrastructure readiness, team capability gaps, and appropriate use case prioritization.
Ready to Discuss Your Project?
Reach out to explore how we can help with your AI implementation needs.
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