Ontology
You can't automate what you don't understand
NumoData Ontology is a semantic digital twin that maps every network relationship, dependency, and service layer into a single, living model. It delivers total network visibility and the AI-ready data quality that powers true closed-loop autonomy.
Networks are getting more complex. What’s your plan to keep up?
Fragmented data with no intelligence
When every system speaks its own language, AI has nothing meaningful to work with. Gartner warns of widespread AI project failure driven by siloed, disconnected data. The result: point automations that go nowhere, and a closed-loop future that stays out of reach.
NOC alarm fatigue
As networks grow in scale and complexity, so does the volume of issues. Without context, issues become noise. Overwhelmed operations teams, mounting customer dissatisfaction, and reactive workflows become the default.
Why a knowledge graph alone is not enough
True network autonomy requires three things working together:
Graph infrastructure to store and traverse relationships across network data.
Semantic modelling to define meaning, rules, relationships, and inferences so that data becomes machine-understandable and able to power autonomous network AI.
Operational intelligence to turn that understanding into real outcomes: impact analysis, automation, and closed-loop decisions.
Autonomous networks demand more than a generic knowledge graph.
AIOps starts with Ontology
AI-grade data quality
Every AI initiative lives or dies on data. Ontology guarantees a qualified, continuously reconciled foundation of network data, so your AI models reason on truth, not noise. Move past the hype. Build on what’s real.
Seamless operations.
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Service impact analysis
Instantly simulate which customers and services are affected by any network event, change, or outage. Act with confidence, not guesswork. Know before your customers do.
Predict failures before they happen.
Predictive incident detection
Stop chasing symptoms. Ontology pinpoints root causes with surgical precision, cutting the costly delays and fractured war rooms that drive customer churn.
Plan better. Grow faster.
Faster service path design
Establish optimal service paths for new customers in seconds. Accelerate order-to-sales cycles and secure new business without the bottleneck.
The understanding that protects every
layer of your network.
Governed autonomous networks
Safeguard unified policies across hybrid human and AI teams. Precise, real-time control of data, logic, and operations keeps your network secure as autonomy scales.
Scale with confidence. Stay secure.
Modernize without disruption
Connect your existing stack without disrupting your ecosystem. Unify semantics without migration and add capabilities without replacing infrastructure.
No rip and replace.
Network intelligence at your fingertips
Introducing Insight Engine. An Ontology-powered GenAI assistant that delivers instant, context-rich answers on network state, service impact, change history, and more. Any team.
Any question. Trusted answers.
Give every team the network knowledge to act with confidence.
Billions of parameters.
Human & AI decisions. One Ontology.
Visualization layer
Real-time network visibility gives AI and Agentic AI the context to act. Human operators stay in the loop to review, override, or intervene only when and where it matters.
Reasoning layer
Applies telecom-aware reasoning, simulation, and validation to analyze service impact, identify root causes, and detect anomalies continuously.
Semantic modelling layer
Flexible, telecom-specific prebuilt models aligned with domain rules and inferences. Creates a context-rich semantic digital twin of the live network.
Ingestion layer
Continuously ingests and reconciles data across the entire network, giving AI a trusted, up-to-date foundation to orchestrate operations.
improvement in Mean Time to Understand (MTTU)
faster Mean Time to
Resolve (MTTR)
reduction in
customer churn
improvement in root
cause accuracy
person-days saved
in network planning
targeted response
to security incidents
Real Solutions. Real Results.
Vodafone: Faster order-to-sales
Vodafone streamlined network planning, improved root cause analysis accuracy by 75%, and increased right-first-time deployments by 35%, accelerating order-to-cash across the business.
Tier-1 US operator
Significant reduction in ticket volume. An 80% improvement in MTTR, 75% more accurate root cause targeting, and a 40% reduction in troubled tickets, driven by Ontology-powered operational intelligence.
Telia
Five operating companies. One Ontology. Telia unified five operations centers on a single Ontology-powered view, federated their inventory, and deployed service impact analysis tightly integrated with ServiceNow, setting the foundation for network autonomy.
MTN
Controlled costs. Unified services. MTN connected 15 operating companies through Ontology, federated inventory, and applied change and vulnerability analysis to cut infrastructure OPEX and strengthen service-level reporting.
Ontology FAQs
What is an Ontology-based semantic digital twin?
A semantic digital twin is a live, vendor-agnostic replica of your entire network. It is built on a knowledge graph that models physical, logical, service, security, and business relationships as a unified semantic layer. Unlike traditional inventory tools, Ontology continuously ingests data from OSS, BSS, cloud, and third-party sources, reconciling and enriching relationships in real time to create a single source of truth for network understanding and decision-making.
How is Ontology different from inventory tools or CMDBs?
CMDBs and legacy inventory systems store flat records in rigid relational databases that require manual reconciliation, a process that is slow and error prone. Ontology uses a live, schema-flexible knowledge graph with a semantic inference engine that automatically federates data from inventory, NMS, CRM, service catalogs, and cloud platforms, linking them semantically into a unified, end-to-end network model.
Do I need to replace my existing systems?
No. Ontology adds an intelligence layer on top of your existing OSS/BSS stack. It ingests data via standard adapters (SNMP, APIs, streaming, file imports) and enriches it without requiring changes to source systems.
Does Ontology support GenAI and agentic AI use cases?
Yes. Ontology integrates with NumoData’s GraphRAG engine to power an AI Knowledge Assistant that responds to natural language queries about network state, service impact, and change history with accurate, traceable answers. The Knowledge Assistant can operate in human-led or fully agentic workflows via MCP/A2A architecture.
What is a "Dark NOC"?
A Dark NOC refers to fully autonomous, agentic network operations, a state where AI agents can detect, diagnose, and remediate issues without human intervention. Ontology serves as the semantic backbone that makes this possible, enabling a shift from reactive operations to proactive, intent-based autonomy.
What data sources can Ontology ingest?
Ontology ingests from a wide range of sources, including Operations Support Systems (OSS), Business Support Systems (BSS), real-time and event data streams, network and service domain data, orchestration systems, asset management platforms, semi-structured data such as device configurations and spreadsheets, and unstructured data such as free-text records and documents.
Can I run multiple digital twins across my network?
Yes, and it is often beneficial. Domain-specific digital twins can accelerate time to value, simplify deployment, and allow teams to focus on specific challenges. However, a multi-twin strategy requires careful governance: consistent data models, semantic alignment, and interoperability are essential to avoid fragmentation. For end-to-end use cases, an overarching twin is typically still required.
Do I need to build a complete network digital twin to see value?
No. Most successful Ontology deployments start with a specific use case or domain, delivering targeted results quickly, then expand incrementally. This approach reduces risk, accelerates ROI, and keeps delivery aligned with business priorities.
How does Ontology handle incomplete or inconsistent OSS/BSS data?
Telecom data is rarely clean or complete. Ontology treats missing information as unknown rather than false, allowing the model to evolve as new data arrives. It uses matching rules and semantic inference to recognize when multiple systems describe the same device, link, or service, resolve conflicts, and surface data gaps, progressively building a more accurate, reliable network view without requiring a single monolithic inventory.