At MetaLearner, onboarding a customer means carefully crafting taxonomies, relationships, and hierarchies across thousands of SAP tables to teach our AI specified customer business logic and relationship. This is ontology development, also known as knowledge engineering. But at a deeper level, as we integrate these ontologies into our Database Agent, we’re doing something mathematically elegant: projecting high-dimensional data into a lower-dimensional conceptual space, much like dimensionality-reduction techniques in linear algebra. We are factorizing features.
In linear algebra and machine learning, factorization involves breaking down complex systems into simpler, lower-dimensional components. It’s how we transform messy, high-dimensional enterprise data into something structured and usable, like identifying latent features in a matrix factorization model.
Ontology achieves a similar kind of compression, but through abstraction and discretization rather than algebraic operations. The first step is deciding what matters. What are the meaningful concepts we care about? What deserves our attention? This act of naming, of defining ontological classes is not merely descriptive. It’s selective. It acts as a cognitive filter.
For example, at a major F&B company, once we made those ontological choices, we effectively projected the chaotic surface of more than 110,000 SAP tables onto a smaller, more meaningful subspace—a conceptual lens. The data was sprawling: SAP tables, transaction logs, guidance manuals, external data APIs, and marketing spreadsheets. But after defining key ontological classes, bill of materials, customers, payments, inventory, material movement, plants and warehouses, production, products, purchases, and sales, we began compressing that enterprise data into a concise set of interpretive dimensions. These weren’t just labels; they became axes of understanding. This was our factorized view of a vast industrial manufacturing operation spanning Latin America and the Caribbean (LAC).
Our MetaLearner Forecast Agent now has something to hook into. Our Database Agent knows what to extract, link, store, and serve. We’ve constrained the entropy of MetaLearner’s Automated Intelligence Stack, not by discarding information, but by organizing it around meaning.
Meta Llama is famously good at handling unstructured data. But its real potential shines when it is coupled with structure, especially when that structure reflects the core distinctions of our clients’ domains. A well-designed ontology functions like feature engineering for knowledge-centric AI. We define priors for latent variables. We choose the concepts that anchor interpretation. We factorize our clients’ data accordingly.
The result? A fully automated forecasting pipeline, more explainable operating results, and a far more coherent internal representation of our clients’ ERP data. This level of customization and precision simply cannot be achieved with generic solutions.
At MetaLearner, ontology is not a documentation task or a knowledge management formality. It’s a strategic, high-leverage move embedded in our Database Agent. Done right, it compresses meaning, factorizes uncertainty, and empowers clients to evaluate scenarios and make resilient operating decisions.