A six-proof framework for turning AI prototypes into adopted business systems.
I’m Sam Upra.
Principal FDE — AI
I turn ambiguous AI and data use cases into production products, connecting business outcomes, architecture, and delivery.
Based in Bangkok · M.S. Computer Science, Georgia Tech · B.S. Computer Science, Mahidol University
Writing
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7 min read
Why Enterprise AI Fails Between Prototype and Adoption
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8 min read
Prompt Versioning in Amazon Bedrock Without Breaking Production
A practical pattern for immutable prompt versions, environment promotion, evaluation, and rollback in Amazon Bedrock.
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8 min read
The Technical Leader’s Real Job: Make Decisions Legible
Why senior technical leadership depends on making context, trade-offs, decision rights, and follow-through visible.
Selected problems
- AI product delivery
- Turning ambiguous use cases into deployable products, adoption plans, and measurable outcomes.
- Data platforms and APIs
- Connecting fragmented data, product, and engineering domains into usable systems.
- Digital transformation
- Translating executive priorities into architecture, roadmaps, and delivery structures.
- Zero-to-one engineering
- Building products, teams, and technical foundations when the path is not yet defined.
Selected experiences
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Project Leader & Business Development Consultant
Leading delivery in Thailand across data platforms, dashboards, APIs, and ML/AI products.
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Co-founder & CTO
Built and ran engineering end-to-end for KOON, a cross-platform fintech micro-savings app.
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Technical Lead — Digital Specialist
Bank digitization, carbon-accounting SaaS, and hospital ecosystem builds across Southeast Asia.
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Senior Backend Engineer
Senior engineer within fintech/payments/ordering domains; drove migration from monolith to Go microservices.
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Staff Full-Stack Software Engineer
Full-stack development for partner-facing services and dashboards.
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Co-founder & CTO
Conversational CX fintech across Singapore, Dubai, and Bangkok. Exited via an IP sale to Advans Myanmar.
How I work
- Start with the outcome. Clarify the decision, user, and operating change before selecting technology.
- Make complexity legible. Architecture and delivery plans should be understandable across executive, product, and engineering teams.
- Design for adoption. A technically correct product has not succeeded until people can use it reliably and repeatedly.
Beyond work
I read non-fiction—especially Walter Isaacson—cook, spend time with my family and enjoy trying new restaurants.