Intelligence infrastructure
Governed domain intelligence for enterprise data, AI product infrastructure, and investment decisions.
Where we work
For enterprise organizations managing post-acquisition integration, multi-system fragmentation, or AI deployments that are underperforming. Hypericum installs governed domain intelligence that replaces inconsistent taxonomies with versioned, auditable structure.
For software vendors whose AI features stall in production because client data means different things across different implementations. Hypericum builds the governed semantic layer your AI operates against, so the feature ships and the outputs can be trusted.
For investors and strategy teams who require proprietary signal and structured market knowledge rather than commodity data feeds. The structured foundation that makes AI-driven investment intelligence defensible and auditable.
Hypericum's research and analysis across enterprise data, AI product infrastructure, and investment intelligence. Perspectives on classification, AI readiness, and the semantic layer that connects them.
Where the gap is
Every AI system depends on the layer beneath it meaning what it should mean. Most tools govern everything except that layer.
Transformation tools move data. MDM governs master records. Semantic layers define metrics. None of them govern what your entities, categories, and concepts mean across systems, clients, or acquired portfolios.
That is the classification layer. It is what your AI operates against. Without it, every system above it, however well built, is reasoning from inconsistent inputs.
Hypericum builds and governs the classification layer other tools depend on but do not provide.
Who we work with
"Our data is structured. It just doesn't always mean the same thing in different systems."
"Our AI features perform in demo. Across our client base, they are not reliable."
"Our investment models are only as good as the classification layer beneath them. That layer isn't governed."
Our team
Technical work led by Professor Scott Deerwester, inventor of Latent Semantic Analysis and a foundational contributor to AI embeddings and semantic search.