Perspectives
Engineering insights, architectural deep-dives, and perspectives on AI, platform engineering, and modern systems.
Model interoperability as regulatory expectation: a stress-exit pattern for AI in financial services
The case for model interoperability in financial services: abstraction layer, regression tests, and tested stress-exit plans, on existing banking precedent.
Beyond monolithic AI controls: a classification framework for financial services
A four-tier framework (Assistive, Augmented, Semi-autonomous, Peer) for proportionate AI governance in financial services, with controls matched to each autonomy tier.
Read MoreDORA Metrics Explained: SPACE & Productivity
Developer productivity has become a polite name for the metric a CIO is allowed to ask about. DORA and SPACE are useful, but only after the harder conversation has been had: whether the work the team is measuring is the right work, and whether the velocity it is chasing buys back the audit cost it generates.
Read MoreEnterprise AI Adoption: Why Finance Lacks Data Strategy
Enterprise AI in financial services is being sold as a model problem. Almost every time we look closely, it is a data problem. Most banks are evaluating models without seriously evaluating where their data lives, what shape it is in, and whether the AI they are buying can do anything useful with it.
Read MoreMulti-Cloud vs Hybrid Cloud for Banks: A Decision Framework Based on What We've Seen Work
For banks, the multi-cloud versus hybrid cloud question is not a preference. It is a constraint, shaped by where regulated data lives, what the legacy stack actually looks like, and how mature the engineering team really is.
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