AI Workflow Automation
I design AI-assisted workflows where models perform bounded knowledge tasks inside a larger operational system with validation, APIs, review states, retries, and observable outcomes.
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.
Teams manually moving information between an LLM and business systems
AI outputs that are useful but inconsistent or difficult to operationalize
Workflows that need AI judgment without giving a model unrestricted authority
AI prototypes with no reliable path into day-to-day operations
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.
AI workflow automation places model capabilities such as classification, extraction, summarization, enrichment, or generation inside a repeatable business process with deterministic rules, integrations, validation, and human review where needed.
Not necessarily. Many production problems are better served by bounded model calls inside deterministic orchestration. Agentic behavior is useful when the task genuinely requires tool selection or adaptive multi-step reasoning.
I separate model judgment from irreversible actions, validate outputs, constrain available tools and data, preserve review states, log useful execution context, and define recovery behavior before launch.
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.