Why We Built ConceptDB: A Letter from the Founder

The Curiosity

When I was young I wanted to understand the world. I wanted to know not just that it worked, but how and why it worked. This was to be a lifelong quest driven by unsatisfiable curiosity.

In college I conspired to build a model of human cognition, a paramount peculiarity that no one could give me a straight answer as to why it worked at all. I studied it from every angle I could find, thinking that if I could triangulate enough perspectives I would be able to pin down the Concept in between. Alas, all I learned was philosophy and entropy.

I didn't know it at the time, but that is all I needed.

The Enterprise Years

I needed money, so I got work building Enterprise Knowledge Management systems, creating Data Warehouses, filling them with arcane SQL, and negotiating the installation of Enterprise Resource Planning systems. As is the destiny of any good engineer, I was pushed into management. I became the team's mouthpiece for negotiations around the way the technology actually worked and the way that people wished it would work.

In 15 years of enterprise data work, I learned the hardest problem isn't technology. It's meaning.

The Insight

It became apparent to me over time that literally no one spent any time arguing about semantics. Everyone could agree on the high-level, often intangible, bright lines of the project. About 30% of any group would engage in spirited discussions around the syntax used to describe a problem, but were quite amiable with proper disambiguations. Meetings involving the interpretation of the syntax were, however, doomed.

Three principles crystallized:

The gap between what people mean and what computers store is where every enterprise data project goes to die. Every failed migration, every dashboard no one trusts, every report that two departments interpret differently: they all trace back to the same root cause, no shared, formal understanding of what the data means.

The Mission

We realized that large language models could bridge the gap between how people think about their business and how computers store data. LLMs are extraordinarily good at understanding natural language and mapping it to formal structures. The missing piece was a system that could take those mappings and prove they were correct.

That is what ConceptDB does. We built it on three pillars:

Your AI. Your Data. Your Rules.

The Vision

We are building the infrastructure layer where AI meets formal reasoning, where every organization can harness the power of language models without sacrificing correctness, governance, or trust. ConceptDB will be the foundation that makes enterprise AI not only powerful but provably reliable.

Read More

• See how our Business Dictionary builds itself from your existing data
• Learn how our Query Engine delivers answers you can prove
• Explore how our Agent Platform gives you visibility into every AI decision

- Datawizz, Founder of ConceptDB