About

I run Tech Teiv, an independent technology advisory and research practice based in Chicago with active engagement across Southeast Asia. My work focuses on the questions enterprise technology leaders face when AI moves from experimentation into the production lifecycle: how to embed it usefully without breaking what works, how to modernize legacy systems on a realistic timeline, and how to scale engineering operating models across geographies and regulatory regimes.

Qanit Al-Syed

Career arc

Until 2026 I was Regional IT Director for a Fortune 100 insurance carrier's Asia-Pacific operations, with engineering and applications oversight across the region. Before that I was CIO for the Asia sub-region, leading a USD 600M+ operation with a USD 24M annual technology budget and engineering teams of approximately 200.

The substantive work over the last several years centered on AI-enabled software development at enterprise scale, legacy modernization in regulated environments, direct-to-customer platform architecture across multiple markets, and M&A integration for commercial lines portfolios. Earlier roles included regional enterprise architecture across the East region (India, Russia, China, Singapore, Hong Kong, Thailand, Vietnam) and software architecture for digital products. The arc moved from hands-on engineering through architecture into regional executive leadership over fifteen years inside the same enterprise.

I moved to independent practice in 2026 to do work that requires senior judgment more than incremental hands — advisory engagements, applied research on AI evaluation methodology, and selective fractional CTO work with technology-forward companies in insurance and adjacent regulated industries.

Education

  • MSc, Islamic Finance — INCEIF University, Malaysia (2025)
  • MS, Software Engineering — Harvard University (2019)
  • BS, Electrical Engineering — University of Michigan (2010)

How I work

I work best where the technical questions and the business questions are tangled together — modernization decisions that are also organizational decisions, AI adoption choices that are also governance choices, architecture work that has to land inside real political and budget constraints. My background is operator-first: I have run the teams, owned the budgets, and lived inside the trade-offs. I bring methodology and frameworks, but they exist in service of decisions, not in place of them.