Bridging Your Data With Strategy
Make the complex obvious
Golden Data ships the AI feature that makes the next step in your app obvious — designed against your real users, evaluated against real failure modes, handed off as production code in your codebase. Two to three weeks.
What We Do
Spec the right feature
I work with you to find the one AI feature that actually changes how your app feels — not the three that sound impressive on a roadmap. Decision-first scoping, with strong taste for sales ops, ad tech, analytics, and BI products.
Build it in your stack
Code that ships in your codebase. Latency budgets, eval harness, cost caps, observability — the boring parts that make AI work in production.
Ship something users notice
No chatbot in the corner. The feature shows up at the moment of decision, makes the next step obvious, and gets out of the way.
The Clarity Sprint
A focused engagement that ships one production-ready AI feature in your codebase — spec'd, built, evaluated, and handed off in two to three weeks.
You bring
- Codebase access and a working dev environment
- The user moment you want to make obvious
- The constraint you've been wrestling with (latency, cost, accuracy, scale)
You get
- Production code in your repo, not on a slide
- An eval harness with golden examples + latency and cost monitoring
- A handoff doc explaining what was shipped and why
Examples
Case Study: Ogden School District
Different domain, same lens. Before AI features, this thinking was applied to institutional documents. The frame transfers cleanly: find the moment of decision, surface the constraint, make the next step obvious.
The Problem: Degree requirements were locked in a dense, static PDF.
Parents had to manually track credits and interpret complex eligibility rules on their own.
The Fix: We transformed the policy into a living, visual roadmap.
Confusion was replaced by a clear status bar and obvious next steps.
"Wall of text" cognitive load.
Hidden constraints.
Visual progress. Clear hierarchy.
Actionable next steps.
Why this worked
- Progress is visible, not inferred
- Constraints surface early, not after mistakes
- Everyone is looking at the same source of truth
About
Who's behind Golden Data
I'm Paul Brown. By day I lead sales operations. On personal time I write about what's working in B2B SaaS and ship small AI features against publicly available problems. My background runs through analytics engineering, senior data analyst leadership, and BI strategy.
Golden Data is my independent practice. It's built outside of work hours, doesn't compete with my employer's business, and uses only publicly available material. All opinions here are my own. Crafting, in particular, is public career-market intelligence — not a recruiting tool, and it does not source from any employer I work with.
What I believe
The goal isn't "more data." The goal is shared understanding. If people can't explain it in 60 seconds, it's broken — and we can fix it.