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 from your asset registers, maintenance logs, and network records, 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. Galaxia, the symbolic engine built by our partner Smabbler, computes that layer straight from your own data: no language model in the loop, and every connection traceable to its source. The graph reflects how you operate, not a generic template.
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. Illustrative of the pattern, not published client references.
An operator captures cascade relationships between assets in a knowledge graph. Maintenance planning shifts from age-based schedules to dependency-aware priorities, and the assets most likely to cause downstream failures rise to the top of the list.
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.
We ingest the data you already hold, straight from the asset registers, telemetry, and maintenance systems you already run. Nothing is replaced. Your teams keep working where they work today.
Galaxia computes a first structure over your real data in hours or days, so you can put questions to it before committing to anything. A scoped 8 to 12 week paid pilot then builds your first decision context, say the failure cascade for a critical asset, against success criteria you set. 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: which assets fail together because they share a feeder, a supplier, or a maintenance history, so the true blast radius of a single fault is visible before it cascades. Those answers live in the relationships across your asset register, maintenance, and operational systems and your inspection reports, manuals, and work orders, 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.
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. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.