Is Palantir right or is it just hype?
You have surely heard the term ontology and why leading companies trust it to build reliable data models. Recent advances in large language models have made it possible for startups and corporations to build flexible and reliable semantic layers faster than ever.
What we mean by ontology
Ontology comes from philosophy and represents a formal catalog of objects, the properties that describe them, and the relationships that connect them. In practical data terms, it means defining entities such as customer, product, or transaction, their attributes, and how they relate to each other. A simple example could describe a beverage, its type, price, and relationship to a sale, which already starts to look like a data model designed for the business.
Ontology as a semantic layer
Palantir popularized ontology as a semantic layer solution that acts as an abstraction between raw data and business concepts. Instead of relying only on isolated relational tables, the ontology captures business logic and ensures that queries respect that model, making it easier for anyone to get answers that faithfully reflect how the company operates.
Why semantic layers are sought after
Terms like single source of truth, golden tables, or semantic layer pursue the same promise: that any user can ask and receive answers consistent with business reality. An analyst should be able to ask how many premium users bought espresso last month and trust the answer. Well-implemented ontologies fulfill that promise by encoding business logic and applying rules to queries.
Classic models and their limitations
Traditional approaches are not new. The star schema of the 1990s centralizes a large fact table with dimension tables on the edges, prioritizing speed and simplicity. The pattern forces data to be classified as facts or dimensions, which can break when reality changes. Snowflake schemas attempt to normalize to reduce redundancy, but at the cost of complex joins and risk of errors. Both approaches assume the business remains stable, which rarely happens today.
Palantir's proposal: turning the model into a graph
Palantir removed the facts versus dimensions barrier and treated each table or entity as a node in a directed graph with typed links and explicit cardinalities. This way, analysts can traverse the graph without guessing how joins should behave, and the model can evolve by adding nodes or edges as business needs change.
Why volatility complicates everything
It is very difficult to build a source of truth that is both solid and flexible enough for an environment of rapid change. A new regulatory regime, a cybersecurity threat, or a product pivot can turn carefully built pipelines into technical debt. Palantir solves this with engineers deployed at client sites who manually maintain the ontology, while many startups accept analytical debt for speed.
LLMs change the rules: automatic ontology generation
Language models can change the economics of generating and maintaining ontologies. Pointed at a data warehouse, they can take a snapshot of the data state and propose a model: identify naming conventions, detect tables that share keys, suggest relationships with cardinality, and even generate initial SQL and pipelines. They do it fast, but they can make mistakes, generalize poorly, or omit exceptions without proper human guidance.
How we help at Q2BSTUDIO
At Q2BSTUDIO, we are a custom software and application development company specialized in artificial intelligence, cybersecurity, and cloud solutions. We offer AWS and Azure cloud services, business intelligence services, custom software development and custom applications, AI agent integration, and Power BI solutions for metric visualization and governance. We implement ontologies and semantic layers by combining automatic generation through language models with expert review to avoid hallucinations and ensure that the customer definition excludes, for example, free trial users when the business requires it.
Our collaborative workflow
We work with a flow similar to extract, draft, review, and democratize. We extract metadata and lineage from the warehouse, the model proposes objects, links, and SQL, business experts review and we approve changes in a Git-like version control, and finally we deliver a semantic layer that democratizes data access. With each query we learn and optimize: repeated joins become reusable pipelines, and costly queries generate recommendations for standard measures across the organization.
Benefits for enterprises and startups
With our solutions, companies manage schema drift without large data teams, and startups access enterprise-level modeling with early-stage budgets. We add cybersecurity and compliance layers for regulated environments and offer managed operations for AWS and Azure cloud services.
Contact us
If you want to explore how a well-designed ontology can improve your decisions, reduce duplication, and accelerate analytics, write to us at hi@q2bstudio.com and let's talk about artificial intelligence projects, AI for business, AI agents, custom software, custom applications, cybersecurity, business intelligence services, and Power BI.
