The Ontology Based Data Platform
Unify your organisation's data into a living, queryable semantic model — a single source of truth where every report, dashboard, and analyst works from the same trusted, business-level meaning, not disconnected tables.
Entity Graph
Model customers, accounts, orders, products, and any domain concept, connected by typed, traversable relationships.
Insight & AI, Connected
Analytics, agents, and reports all anchor to the ontology — every metric defined once, and AI grounded in governed meaning for accurate, low-hallucination answers.
Trusted & Governed
Consistent definitions, lineage, and access control make data discoverable and dependable for self-service across teams.
An ontology based data platform is a semantic data platform that unifies an organisation's data into a living, queryable semantic model — a graph of entities, relationships, and rules layered over your existing data. Instead of disconnected tables and siloed systems, every analyst, dashboard, report, and AI agent works from one consistent, business-level source of truth, making data discoverable, trustworthy, and ready for both insight and AI.
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. Scrydon's sovereign ontology based data platform is the connective semantic layer: it models your real-world entities and the typed relationships between them, applies consistent definitions and governance, and keeps everything current as new data lands. The result is faster analytics, self-service insight, and metrics that mean the same thing everywhere — and a semantic foundation that grounds AI agents in your real business meaning, the essence of ontology AI.
Ontology Based Data Platform in the Scrydon platform
One integrated, sovereign architecture. Here is where Ontology Based Data Platform sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents to enable your processes
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Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
Ontology Based Data Platform in depth
Insights
Data sitting in warehouses and dashboards that nobody reads is data they can't use. The Insights layer changes that — giving the right people the right information without them having to ask for it. Every metric is anchored to the Cognitive Enterprise ontology, so a revenue figure doesn't arrive in isolation. Data in context — not just in dashboards.
Decision-makers get a live view of the enterprise — financial performance, operational health, procurement status — without waiting for a data team to prepare a report.
- Interactive notebooks: Python and SQL environments with full access to your lakehouse data — no data movement required.
- Visual dashboards: Pre-built, always-current reporting updated automatically as the business moves — no manual refresh, no stale numbers.
- Agent-native analytics: AI agents can query, summarise, and act on insights autonomously — closing the loop between analysis and action.
Cognitive Enterprise
Link your processes, knowledge & data to ontologies.
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. The Cognitive Enterprise layer is the defining intelligence of the AI OS: a living, queryable semantic model of your organisation's entities, processes, and rules. It is the single source of truth that allows every agent, analyst, and workflow to reason about your business with a consistent understanding.
Without it, AI agents reason on noise. With it, they reason on the business.
- Entity graph: Model customers, accounts, orders, products, and any domain concept — then connect them with typed, traversable relationships.
- Process integration: Link real-world workflows to ontology entities so agents understand how data flows through your business.
- Continuous enrichment: Agents automatically enrich ontology nodes with fresh data from the lakehouse, keeping the model current without manual effort.
The Lakehouse is the high-performance data foundation underpinning the Cognitive Enterprise. It is built on StarRocks — a blazing-fast, vectorised MPP query engine delivering sub-second analytics, real-time updates, and high concurrency — and queries open Apache Iceberg tables directly, merging the flexibility of a data lake with the speed of a warehouse under a single, sovereign roof.
- Open Iceberg tables: Query Apache Iceberg and other open table formats directly — your data stays yours, with no proprietary lock-in and no data movement.
- Lightning OLAP: StarRocks' vectorised engine, cost-based optimiser, and materialised views power real-time SQL — from dashboards to agent reasoning — without data duplication.
- Integrated Vector Search: Store and query embeddings alongside traditional data, making the Lakehouse instantly ready for AI workloads.
A single source of truth for every question
An ontology based data platform sits above your raw data and turns it into meaning. Instead of joining tables by hand for every report, every analyst, dashboard, and application shares one consistent, queryable understanding of the organisation — so insight is faster and metrics are consistent.
Entities — Model the real concepts in your business — customers, assets, cases, products.
Relationships — Connect them with typed, traversable links that mirror how the business actually works.
Definitions — Define each metric and rule once, so figures reconcile across every report and team.
Live data — Keep the model current by continuously pulling fresh data from the lakehouse.
From scattered tables to trusted insight
Raw tables and BI extracts leave every team to re-derive what the data means — and to disagree about it. An ontology based data platform makes meaning explicit: shared entities, relationships, and definitions that turn disconnected data into discoverable, governed, self-service insight. Analysts answer questions in minutes instead of weeks, dashboards reconcile, and the same trusted foundation is ready for whatever comes next — including AI.
The semantic foundation that grounds AI
AI agents are only as trustworthy as the meaning they reason over. Ontology AI is the practice of grounding models in a semantic data platform rather than raw tables: the ontology gives agents the same governed entities, relationships, and definitions your analysts use, so they retrieve the right data, answer in business terms, and stay auditable. It is how a semantic layer turns a general model into a system that understands your organisation — and it sharply reduces hallucination because every answer traces back to a defined concept.
Grounded retrieval — Agents query the ontology, not loose tables — so they pull the right entities and relationships every time.
Shared definitions — AI uses the same metric and rule definitions as your dashboards, so people and agents agree on the numbers.
Explainable answers — Every AI answer traces back to defined concepts in the semantic model, keeping reasoning auditable.
One sovereign foundation — The same semantic data platform powers analytics and AI inside your perimeter — no separate, ungoverned AI data copy.
Ontology based data platform vs Databricks, Fabric & Palantir
The major data platforms each take a different route to insight. Scrydon leads with a sovereign, business-level ontology over your existing data — combining a true semantic layer with European data sovereignty.
| Capability | Scrydon | Databricks | Microsoft Fabric | Palantir Foundry |
|---|---|---|---|---|
| Primary focus | Ontology-based data unification and trusted insight | Lakehouse for data engineering and ML | Unified SaaS analytics and BI | Data integration with an operational ontology |
| Semantic / ontology layer | Native, first-class business ontology | Limited — catalog and metric views, not a business ontology | Power BI semantic models, scoped per dataset | Strong — Ontology is core to the product |
| Analytics & insights | Insights anchored to the ontology; consistent metrics everywhere | SQL and BI on the lakehouse | Deep BI through Power BI | Built-in dashboards and analytical apps |
| Deployment & sovereignty | Sovereign — air-gapped to cloud, European-native | Cloud (AWS / Azure / GCP) | Azure cloud SaaS only | Cloud or on-prem, US vendor |
| Openness & lock-in | Open formats, your perimeter, low lock-in | Open Delta format, platform-centric tooling | OneLake and the Microsoft ecosystem | Proprietary, high lock-in |
| Best fit | Organisations needing a sovereign semantic layer for insight | Data engineering and ML at scale | Microsoft-centric BI teams | Large enterprises and government, at premium cost |
Comparison is Scrydon's summary for orientation. Databricks, Microsoft Fabric, and Palantir Foundry are trademarks of their respective owners; capabilities evolve — verify current details with each vendor.
Frequently asked questions
What is an ontology based data platform?+
Is an ontology based data platform the same as a semantic data platform?+
What is ontology AI and how does an ontology improve AI?+
How is an ontology based data platform different from a data lake or data warehouse?+
How does an ontology based data platform compare to Databricks, Microsoft Fabric, and Palantir Foundry?+
How does an ontology based data platform improve analytics and reporting?+
How does an ontology based data platform stay up to date?+
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