We spent our careers inside the problem.

FloFrame began as an architecture and design practice. The two people at its technical core had already spent their careers building enterprise systems: Craig Cunningham across thirty-seven years at IBM, from field engineer to Executive IT Architect on Walmart, PepsiCo, Northrop Grumman, and Lockheed Martin, then as CTO of the largest crop consulting company in the United States; David Loving across four decades of production software, from Autodesk's early AutoCAD years, to twelve years as the database and API engineer on the PETRA geoscience platform, to graph databases, data lakes, and industrial time-series data at Phillips 66. They had built the table-shaped world at every generation of the hardware.

The problem they kept meeting.

Every one of those systems stored real-world information by flattening it into rows. That construct was never designed for the real world. It was designed for the storage media of the 1960s: punch cards, then tape, then spinning disks, where fixed-width records and sequential access were the physical limit. The hardware constraint disappeared decades ago. The paradigm never did. Sixty years later, most of the effort and most of the risk in a modern application still goes to translating information into a table and back out again. Schemas, migrations, joins, and a large share of the security surface are all artifacts of that translation.

First principles.

Rather than add another layer on top of the old model, they asked the question underneath it: given today's infrastructure, what does a piece of information actually need in order to be stored, secured, and retrieved? The answer is small. An identity. A type. An owner. Everything else is the object's own business. From that they designed an infrastructure where storage is blind to schema, where tenancy and authorization are structural rather than bolted on, and where the application never bends its shape to fit the store.

Proved before it was sold.

The architecture was not validated on a whiteboard. It was hardened inside real applications: a full field service management platform, a geospatial data product, and TUOCA (The Upload Offline Conversions App). Three years, multiple applications, zero schema migrations. A small senior team, working outside the conventional process, shipping a working system before anyone had agreed it should be possible.

Then an operator joined the architects, and he had already built the thing the architecture was for.

Jon Cunningham spent thirteen years in home services and property technology, the last eight at OGD Overhead Garage Door, where he held both the CMO and CTO roles, one executive over demand, development, and IT, while the company grew from a $14 million regional operator to a national platform, sold to private equity in 2024, and passed $120 million in revenue by the time he stepped down in 2026.

In his final years there he built an AI operating layer for the company: a dozen operational and marketing systems unified in a cloud warehouse, a governed knowledge base for the business context that no transactional platform holds, and a library of shared AI skills that let teams across the company move from question to diagnosis to recommended action. It worked. It also taught him exactly what it costs to make organizational context reliable for AI when that context lives in a dozen disconnected, table-shaped systems: conflicting identifiers, brittle integrations, ungoverned reference data, provenance, permissions, and reconciliation without end.

He brought the first product with him: the offline conversion problem he had fought for years as a CMO became TUOCA. And he brought the conviction that the foundation he wished that AI layer had been standing on should exist for everyone.

What the company is now.

The infrastructure is the foundation, not the product. FloFrame partners with industry subject-matter experts to find the workflow gaps that generic software leaves open and closes them with purpose-built solutions. We build the workflow that works for that context, not a configurable platform that half-fits everyone. Because the architecture was designed first, delivery is predictable, the result is secure and enterprise-ready by construction, and the value-to-cost ratio is something larger vendors cannot match.

How we build.

Senior architects paired with agentic coding inside a zero-trust model. AI has made software cheap to produce. It has not made it trustworthy. Our architecture is what makes AI-assisted development safe at production quality: generated code cannot break tenancy, cannot drift the schema, and cannot reach data it isn't entitled to, because those guarantees live below the application. That is how a company this small delivers at this speed without trading away stability.

Craig designed the engine. David built it. Jon drove the old one until it broke, and knows exactly what the new one has to survive.

Meet the founders