Rick Huang
Rick's Bio
Applied AI grounded in reliable data and measurable business outcomes.
I'm Rick, an Applied AI Architect, Business Lead and Independent Advisor to FlowVision. I specialize in applied AI: helping businesses understand what's possible, choose the right tools, and build applications whose quality, usefulness and operating costs can be measured.
I help teams build their AI knowledge and evaluate models that fit their needs, including frontier and open-weight models. The choice starts with the business problem, the quality the task demands, and the economics of running it at scale. Capability and cost need to be measured on the actual workflow.
Success means more than deploying AI. It means putting it to work in real workflows, measuring the value it creates, and making the cost savings count. All of that depends on a solid data foundation and a deep understanding of how the business works.
My work spans a wide range of industries, each with its own workflows, constraints, and definition of value:
- Consulting
- Consumer CPG
- Healthcare
- E-commerce
- SaaS
- Banking
- Crypto
In practice, I've built agents that help certify data models, connected AI to governed business metrics, and turned hundreds of pages of regulation into knowledge a model can cite. I care about whether an application is useful, how reliably it works, and what it costs to deliver each result.
Outside work I build small, sharp tools: Lazy Journal, a local-first life tracker; an analytics studio called WayAnalytics; and a handful of self-hosted agents that run my chores. Most of them start as a fix for my own annoyance.
FlowVision
I serve as an Independent Advisor to FlowVision. My perspective comes from connecting AI architecture with business requirements: which tasks matter, what data they need, how to evaluate a model, and what it takes to operate the resulting system.
My writing here reflects my own views. Model availability, deployment options and commercial terms should be confirmed with FlowVision.
Media and speaking
For interviews, podcasts and speaking invitations: rick.huang@hzy230051.com.
Useful topics include analytics agents, governed metrics, data architecture and evaluating open-weight models. Get the short bio and portrait →
Where I go deep
- AI education and model selection: helping teams understand and access frontier and open-weight models, and evaluate the tradeoffs in capability, cost, and deployment for their use cases.
- Applied AI systems: agents with defined jobs, tools connected to business systems, document-to-agent knowledge packs, and evaluations that measure whether the application works.
- Enterprise value: identifying useful applications, integrating them into real workflows, and evaluating quality and operating costs against the business outcome.
- Data and business foundations: reliable models of customers, products, operations, and finance; governed metrics and data contracts; and a clear understanding of the decisions they support.
What I believe, roughly
- AI should earn its place through measurable business value and cost savings.
- A metric without a definition is an opinion.
- An analytics agent without evals is a demo.
- The best AI products will feel like better defaults, not like talking to a robot.
- Most of life's compounding happens in time and attention before it shows up in money.
- Money and belonging run on two different accounting systems.