When a critical asset fails, your operations team needs to know in minutes which substations are affected, which customers lose service, and which SLAs are breached. Today that picture lives in tribal knowledge, asset registers, and maintenance logs that no one can reconcile under pressure. LangOptima turns it into a queryable graph.
Built on CIM (IEC 61970), the grid's own semantic standard, so network, asset, and sensor data connect without re-platforming the systems you already run.
Your most experienced engineers carry the map in their heads. Asset registers, SCADA, maintenance logs, and GIS systems each hold part of it. No single system holds the whole.
The people who know which assets depend on which are the ones you can’t afford to lose. Their knowledge is not captured in any system, until the day they retire.
Asset registers, maintenance logs, operational telemetry, and regulatory obligations all live in separate systems with separate identifiers. Reconciling them is a weekend project, not a real-time capability.
When an asset fails, the blast radius is often estimated in meetings, not calculated from data. Teams can underestimate the impact, sometimes by a factor of three.
LangOptima models your assets, dependencies, downstream systems, customers, and regulatory obligations as connected entities. Your SCADA, GIS, asset register, and maintenance systems stay in place. The graph sits above them, capturing the relationships that used to live only in tribal knowledge.
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 →
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.
Asset registers, maintenance logs, SCADA, GIS, and regulatory data unified in a semantic layer. Tribal knowledge captured as structured relationships, not PowerPoint decks.
Surface the full cascade: which assets depend on which, which customers sit downstream, which SLAs and regulatory obligations are at stake. Maintenance history overlays the dependency chain automatically.
Outage response, maintenance prioritization, and capital planning all move at the speed of a query. Decisions happen with data, not gut feel.
Your senior engineers stop being the only source of truth. The graph captures what they know, so every operator benefits from it and succession becomes survivable.
Representative scenarios for energy and utility operators with your profile. Ask us what applies to yours.
An energy company reduced unplanned outage impact by 35% through proactive dependency mapping. Capturing cascade relationships in a knowledge graph let the maintenance team reprioritize work around assets that were about to cause downstream failures.
A single transformer failure typically cascades further than operations teams expect, often affecting three times as many customers. Surfacing this upstream, before the failure, is the core value of the knowledge graph approach.
However complex the data landscape underneath, the engagement itself stays simple.
Pick one decision that matters, say the failure cascade for a critical asset. We build the knowledge graph over the asset registers, telemetry, and maintenance systems you already run. Nothing is replaced. Your teams keep working where they work today.
Your team's questions, reports, and AI run on that connected layer. Answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
A working result in a scoped 8–12 week paid pilot, measured against success criteria you set. If it proves value, you expand from there. If it doesn't, it doesn't scale.
Start with a 30-minute conversation. Tell us about the asset, the cascade, or the planning decision that is hardest to support with data today, and we’ll show you how a knowledge graph would answer it.
The same connected-data approach, applied across sectors. Explore another industry.