Network inventory, service definitions, customer circuits, and change and incident history live across separate systems. LangOptima ingests the network and service data you already hold and connects it into a single queryable knowledge base, so teams trace impact and root cause across the whole topology in seconds, each hop cited back to its source.
Put into production by Telstra as its Knowledge Plane, modeling network topology and operational knowledge as one graph above the OSS and BSS systems already running, rather than as scripted rules. It won a TM Forum Excellence Award. Orange documented the same approach with NORIA, for anomaly detection and incident response.
Physical inventory, logical services, and customer circuits sit in separate tools. In many operators, the end-to-end path from a customer service down to the equipment under it is still reconstructed by hand, often mid-outage.
Physical inventory, logical services, and customer circuits sit in separate tools. The path from a customer service to the equipment beneath it is joined together manually, every time it is needed.
When an element fails, the question is which services and customers it affects. Answering it means joining inventory, service, and circuit data by hand, often after customers have already called in.
A planned change touches one element, but what depends on it is spread across systems. The blast radius of a change is often discovered in the maintenance window, not before it.
LangOptima ingests the network and service records your organization already holds, including physical inventory, logical service definitions, customer circuits, and change and incident history, and structures what is inside them: elements, services, circuits, sites, and the relationships between them. It sits above your inventory, operational-support, and ticketing systems without replacing them, so a question that once meant joining systems by hand becomes a single traversal from an element to every service and customer above it, with the source attached.
Modern AI can structure a great deal on its own. Where it stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value lives. We structure that layer with you on open, world-standard semantics, not a proprietary schema, so the graph reflects how you operate and stays yours: no vendor lock-in, portable to whatever you run next. More on the structure beneath it →
Physical inventory, service definitions, circuits, and incident history unified at the semantic layer. Nothing moves. Nothing is replaced.
Traverse from a failed element to every service and customer it carries, from a planned change to everything downstream, from a cluster of complaints to the shared element behind them.
“Who does this affect, and what depends on it?” answered in seconds, with the records to back it. For network operations and planning teams alike.
Every operations engineer and planner works with the full topology behind them, so impact and change questions stop waiting for the one person who holds the map in their head.
Representative scenarios of how network teams apply a knowledge graph over their inventory and service data. Illustrative of the pattern, not published client references.
An operator connects its inventory, service, and circuit data in a knowledge graph. When an element fails, the team traverses from it to every service and customer above it, each hop citing its source, instead of assembling the picture manually in the middle of an outage.
Before a planned change, the team asks what depends on the element being touched. With dependencies modeled as connected data, the downstream services and customers come back as one query, traceable to the underlying records, instead of a manual review that misses edges.
However complex the data landscape underneath, the engagement itself stays simple.
You don't have to boil the ocean. Start with a single business context and prove it there. Once that foundation is laid properly, the same connected data tends to open opportunities in other departments, so the next team builds on the work already done rather than starting from zero.
We ingest the content and data you already hold, straight from the systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering. If it proves value, you expand from there. If it doesn’t, it doesn’t scale.
You query the connected layer in plain language, and answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
Curious what disconnected data may be costing your organization? The free Data Silo Cost Calculator puts a number on it in about two minutes, with no signup to see the result.
Try the Data Silo Cost Calculator →For a single document with a single answer, plain search or an LLM pointed at your PDFs does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: what a planned change or a single failure affects once you trace it across network inventory, services, and circuits, so the impact across the whole topology is clear before you act. Those answers live in the relationships across your inventory and operational systems and your engineering records, not inside any one of them. A knowledge graph connects the structured systems and the documents into one model, follows the chain across them, and returns each answer with a citation back to the record it came from. Something you can act on, and defend.
A 30-minute conversation about your network and service data landscape: which systems hold your inventory, services, and circuits, and which question a connected view of them would answer first. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.