Vector Embeddings as Objects: Inheritance in Action
The Problem
Most databases treat vector embeddings as blobs—opaque arrays of numbers with no structure. That makes them hard to govern, impossible to extend, and disconnected from the rest of an enterprise’s data model. When embeddings live in isolation, developers lose the ability to understand where they came from, how they relate to other data, or how they can evolve over time. The result is systems that work for small experiments but collapse under enterprise demands.
FlexVertex takes a different view: embeddings should be first-class objects that inherit structure, context, and meaning—just like the data they represent. Instead of treating embeddings as stray mathematical artifacts, FlexVertex binds them into bundles, where they can live as part of a broader model.
Why It Matters
Enterprises don’t operate on flat blobs. Contracts inherit from master templates, customer records extend from shared schemas, and policies follow hierarchies. If your AI substrate can’t reflect those relationships, you end up with fragile hacks that don’t scale. The moment business rules change—or a compliance audit arrives—these brittle workarounds show their cracks.
When embeddings are reduced to raw vectors, teams lose the ability to enforce governance, trace lineage, or evolve models without rewriting pipelines. This is technical debt waiting to happen. In regulated industries like finance, healthcare, and government, the inability to prove where embeddings came from or how they were derived can bring entire projects to a halt.
AI isn’t just about retrieval speed or model accuracy. For enterprises, it’s about trust. If stakeholders can’t trust the foundation, they won’t trust the results—no matter how clever the algorithms seem.
The FlexVertex Answer
FlexVertex makes embeddings object-oriented. Each embedding is part of a bundle, and that entity can inherit structure and properties from its parent. The result: AI infrastructure that grows as your data model grows. When new policies, rules, or structures are added to your business, the underlying AI model evolves naturally along with them.
Because FlexVertex is multi-model at the core, embeddings sit naturally alongside documents, graphs, time series, and key/value data. They connect to everything else—securely and consistently—without bolt-on workarounds. That consistency eliminates the need for custom glue code and ensures that AI applications developed today will remain adaptable tomorrow.
An Example
Think about a legal contract. A standard template defines the boilerplate, while specific agreements inherit and extend that template with unique terms. In most systems, embeddings would flatten these into separate, unconnected blobs. In FlexVertex, each contract bundle inherits context from its template, keeping both structure and meaning intact.
The same principle applies whether you are modeling a product catalog, healthcare records, or research data. A patient encounter inherits from a patient record, while lab results and imaging extend it further. A product inherits from its category, with SKUs and subassemblies layered in. In every case, inheritance isn’t academic—it’s how the real world works.
By reflecting those tangible structures, FlexVertex makes AI both more accurate and more explainable. Stakeholders can trace not just what the system answered, but why—because the context is baked in.
The Takeaway
Embeddings should not live in isolation. They need to behave like the data they represent: structured, connected, and extensible. Flat embeddings may work for toy problems, but enterprises need AI built on models that reflect their actual operational complexity.
FlexVertex brings inheritance to embeddings, making object-oriented AI infrastructure a reality. By elevating them to first-class citizens, we eliminate fragile hacks and provide enterprises with a substrate they can trust—scalable, explainable, and ready for the future.