PSM Design · Selected Engagements
Most case studies are written as isolated success stories. This isn’t that.
Over the last three decades, I’ve worked across industries — retail, healthcare, education, professional services, and enterprise technology environments — solving what initially appear to be very different problems. But underneath them, the pattern is always the same:
What changes over time isn’t the problem. It’s the systems we build around it — and what follows is five engagements, across five industries, that all trace back to the same root cause.
Across every engagement — regardless of industry, company size, or technology stack — the same underlying symptoms show up:
Most business problems aren’t actually business problems. They’re systems problems that haven’t been recognized yet.
That reframe changes everything about how an engagement starts. Instead of asking “what software do we need,” the real question becomes “where has our system stopped matching how the business actually works.”
Across each engagement, the work followed a similar transformation:
→ Unified systems
→ Behavioral intelligence
→ Automated processes
And, ultimately: from reactive decisions → adaptive systems.
This shift is what I now define as Iterative Intelligence:
Systems that continuously improve through real-world feedback loops between human behavior and automated processing.
It isn’t a product. It’s a design principle — one that shows up in every one of the engagements below, whether the industry was retail, healthcare, or higher education.
Read: The Iterative Intelligence CycleSystems are implemented at a point in time — but businesses don’t stand still. New tools are added. Processes change. But the underlying system architecture doesn’t evolve with it.
So the organization begins to drift:
Until eventually:
The system is no longer supporting the business — the business is supporting the system.
That single sentence describes nearly every engagement in this collection before the work began. The businesses weren’t failing. Their systems had simply stopped evolving with them.
What follows are five selected engagements spanning retail, healthcare, professional services, higher education, and e-commerce. Each is a different industry, a different scale, a different starting point — and each is an iteration of the same underlying approach.
The Challenge: Physical retail environments lacked visibility into customer behavior and marketing impact.
The Work: Architected a distributed intelligence platform and led a globally distributed engineering team, enabling real-time attribution by unifying behavioral, transactional, and marketing data — delivered within a constrained ~$60K/month development budget.
The shift wasn’t better reporting. It was the ability to see cause and effect in real time — something most physical businesses had never had.Read Full Case Study →
The Challenge: A fragmented CRM and communication system limited sales effectiveness and required heavy support overhead.
The Work: Rebuilt customer and communication systems across a multi-location, international operation, driving ~20% revenue growth (from ~$10M to $28M through expansion and system scalability) while reducing support staffing requirements by ~50%.
This wasn’t a CRM upgrade. It was the moment communication became intelligent — and the business stopped scaling through headcount.Read Full Case Study →
The Challenge: Legacy systems across communications, CRM, and infrastructure limited the firm’s ability to operate efficiently.
The Work: Led a full-scale modernization of the company’s technology stack, replacing legacy telephony, rebuilding core systems into a unified web-based CRM, and integrating communications, document management, and enterprise search across all offices.
A fully integrated platform introduced early machine learning–driven search and classification, significantly improving information access and operational execution — years before “AI” became mainstream.
Before “AI” was a category, the system was already learning — surfacing insight from data most teams didn’t even know how to use.Read Full Case Study →
The Challenge: Outdated, monolithic systems created performance bottlenecks and limited scalability across multiple university partners.
The Work: Rebuilt a modular platform architecture supporting a $50M annual marketing ecosystem, reducing load times by 40% across all properties, improving Google performance scores from ~50 to 98, and driving sustained ~10% annual revenue growth over five years.
The breakthrough wasn’t performance — it was alignment. Once the system matched how the organization actually operated, everything accelerated.Read Full Case Study →
The Challenge: Large-scale e-commerce platforms struggled with catalog complexity and legacy infrastructure.
The Work: Rebuilt search architecture and deployment systems, reducing processing time by 50%, increasing annual revenue by 20% (~$2.4M on $12M baseline), and lowering operational overhead by 20% — without increasing headcount.
At scale, the problem isn’t inventory — it’s navigation. When users can’t find what they need, the system — not the catalog — is what’s broken.Read Full Case Study →
These are not isolated wins. They are iterations of the same underlying approach:
This is not about implementing tools. It’s about building systems that learn.
What once required large teams, extended timelines, and significant budgets can now be implemented faster, more efficiently, and more intelligently — because the architecture itself is designed to keep evolving rather than needing to be replaced every few years.
This is the foundation of my current work:
Diagnose where your systems have drifted from how the business actually operates.
Learn MoreIntroduce feedback loops and intelligent automation without replacing everything at once.
Learn MoreBusiness operating systems built on Iterative Intelligence, so they keep improving after launch.
Learn MoreEach engagement is built on the same principle: your systems should evolve with your business — not hold it back.
The systems that succeed aren’t the ones that store data — they’re the ones that learn from it and evolve with the business.
Most case studies end with a metric. This one ends with a question: is your system still supporting the business — or is the business now supporting the system?
PSM Design — Human-guided AI and automation strategy for organizations building intelligent business operating systems.