The ontology-powered
AI platform for telco

A data lake is primarily storage: it centralizes raw and curated data (events, logs, CDR/EDR, customer records) so you can query and analyze it. But a lake doesn’t inherently resolve meaning—it won’t tell you how “customer,” “product,” “balance,” “order,” or “session” should be interpreted consistently across BSS/OSS/network, nor does it capture the process and action semantics needed for execution. An ontology is a semantic layer / executable knowledge graph: it normalizes entities, relationships, processes, and actions into governed definitions so AI can reason and safely orchestrate work across systems. Totogi Telco Ontology can use any storage (including your own S3 data lake) and retains no data—it’s the meaning-and-action layer that sits above your data.

A data lake can help, but it doesn’t resolve semantic inconsistency by itself. A data lake centralizes data, and solves the the data availability and access problem. But in order to effectively reduce semantic inconsistency, a data lake must be paired with robust data governance, data cataloging, and a semantic layer. But those are extra semantic disciplines layered on top of the lake, rather than inherent properties of storage. Semantic inconsistency in telco is usually bigger than data format: systems barely agree on meaning, and the same entity/process/action is defined differently across domains and vendors. Totogi Telco Ontology unifies data, logic, and actions into an executable semantic layer, maps each system once into canonical definitions, and lets AI operate from one a digital twin of your telco – semantic model that represent your real-world telco in a machine-readable format. It can sit above (and work with) your existing data lake. The Totogi Ontology is not a copy of your data. It’s the operational interface to your systems. It encodes what things mean, how they relate, which actions are valid, and what the business rules are — across every system you run. When AI acts through the ontology, it acts on controls, not copies. Invalid actions aren’t flagged after the fact. They’re architecturally impossible to attempt.

No. The Totogi Telco Ontology is designed as an overlay, not a replacement. You connect it to your current environment, map semantics without migrating everything, and add new AI-driven capabilities without disrupting what already runs. This is especially valuable in multi-vendor estates where transformations often stall due to integration burden and unclear ownership of business logic. The goal is to modernize “from the top” by standardizing meaning and actions first—then improving execution speed and safety across existing systems.

It means the ontology doesn’t just document entities—it connects entities, processes, and actions from siloed telco applications into a single semantic digital twin of your business. It encodes what things mean, how they relate, which actions are valid, and what the business rules are — across every system you run. By doing so, it enables AI to move from insight to execution, instead of generating recommendations. AI can orchestrate coordinated actions across systems (e.g., CPQ + charging + care + network) based on governed definitions and guardrails. This collapses the insight-to-action gap from weeks of manual work to seconds of orchestrated execution.

Totogi Telco Ontology replaces brittle point-to-point integrations with a simpler model: map each system once into canonical definitions, then govern and reuse those semantics everywhere. In practical terms, integration complexity drops from N² to N because systems stop translating directly to each other—each one maps into the ontology. That also means AI reads from one coherent semantic model instead of reconciling conflicts across multiple schemas, APIs, and data meanings.

Totogi operationalizes telco standards by normalizing entities and actions to TM Forum frameworks (including ODA, SID, and eTOM) and 3GPP concepts where relevant—turning “canonical models” into executable semantics across BSS, OSS, and network systems. This isn’t a slide-only compliance story; it’s about creating a common language so integrations, governance, and AI execution can scale across vendors and domains without constant rework.

You can bring any AI technology—agentic systems, RAG, or chat apps—and plug in your preferred LLMs. Totogi’s approach is to make any LLM “telco-smart” by providing structured context and semantic guardrails from the ontology, rather than betting on a one-off “telco LLM” project. As models evolve, your semantic IP remains stable, because the ontology is the durable layer that keeps meaning, policies, and execution constraints consistent.

Telco connectors integrate with any telco application (including vendor and homegrown systems) without rip-and-replace. They retrieve the entities, business logic, and real-world actions of each application—plus operational knowledge about how people run it—then map that into the ontology. The key idea: “connect once, understand forever.” Instead of building hundreds of fragile integrations, each system maps once to the ontology, and becomes reusable across new workflows, channels, and AI agents.

The outline highlights three core use-case clusters: Understand & Preempt: unify events and entities across BSS/OSS/network so leaders act on one consistent reality (“semantic observability”). Migrate & Consolidate: build a formal map between legacy and target systems, use AI to propose/validate mappings, and simulate migrations before cutover to reduce risk and timelines. Create & Modernize: stop treating “new apps” as integration projects; let teams describe intent while AI generates APIs/flows/agents guided by shared semantics.