AI Systems Architecture
I help teams move from isolated AI features to a coherent operating architecture with clear interfaces, data ownership, provider boundaries, observability, and documented technical decisions.
Janna Wong · AI Automation & Systems Architect · Remote · Global · async-first
Where I help
The implementation starts with the operating problem and its failure modes, then moves into tools and architecture.
Multiple AI tools with duplicated logic and inconsistent data
Provider-specific code that makes model changes expensive
Unclear ownership between application, automation, data, and AI layers
MVPs that work in demos but have no production operating model
Deliverables
Technology
I do not force every system into the same platform. The goal is the lightest architecture that remains reliable, observable, documented, and maintainable.
FAQ
Direct answers written to be useful to both technical buyers and search/retrieval systems.
Usually when AI has moved beyond a single experiment and begins touching multiple workflows, data stores, providers, teams, or customer-facing systems. At that point, interfaces and operating controls matter as much as prompts.
Yes. Where it creates practical value, I separate application behavior from provider-specific implementations so models can change without rewriting the rest of the product.
Related expertise
Send the current process, tools, constraints, and desired outcome. I'll review the context asynchronously and identify the most practical technical next step.