A smart grid needs a brain, not more alerts
Networks are graphs. Knowing that a fault at one node will cascade to others takes reasoning over topology, not threshold alerts. Archisus models your network as a knowledge graph and reasons over it within your safety and regulatory rules.
The challenge
Sensors everywhere, alerts understood nowhere
Electricity distribution networks, water utilities and smart-grid operators collect vast amounts of telemetry, but an alert only means something in the context of the network, the assets and the regulations around it.
Alert floods
One fault produces many downstream alarms that hide the true cause.
Regulated maintenance
Maintenance intervals and asset replacement cycles are set by regulation.
Risky switching
Switching operations have downstream impacts that must be understood before execution.
Use cases
Where Digital Experts help
Nodes, lines and substations are modelled with their connections and constraints, and every recommendation is checked against them.
Fault diagnosis
Sensor alerts interpreted in the context of the grid topology, tracing upstream causes and suppressing downstream noise.
Outage root-cause analysis
Traversal of the topology graph to identify probable fault locations and cascading risks.
Predictive maintenance with regulatory context
Failure prediction combined with the regulatory maintenance intervals encoded as SHACL constraints.
Asset management compliance
The asset portfolio continuously evaluated against regulatory age, condition and replacement-cycle requirements.
Switching impact analysis
Downstream impacts of proposed switching operations evaluated on the network model before execution.
Automated dispatch
Work orders generated, parts checked and crews scheduled within the encoded safety rules.
What changes
What changes for network operators
- Faster restoration by isolating the true fault sooner
- Fewer unnecessary truck rolls
- Higher reliability with maintenance driven by condition and regulation
- Regulatory reports produced from the live knowledge graph
- Operator-ready explanations for every recommendation
- Existing SCADA and asset systems queried in place
Questions
Frequently asked questions
Does it replace our SCADA or ADMS?
No. It reasons over the data those systems already hold, through semantic adapters that query them in place.
Do we need to move our data to a new platform?
No. The federated layer queries your existing systems where they are, such as SAP, SCADA historians, ERPs and document stores. Only query results move, inside your own perimeter, and the models run on-premise or in your private cloud.
We already use an LLM or Copilot. Does this replace it?
No. The ontology layer works with whichever language model you use. The model handles language; the ontology and reasoning engine check its outputs against your rules before they are released.
How do we start?
With the Business DNA Audit: a fixed two-week engagement that maps the rules of one domain, reports the gaps and contradictions, and gives you a draft ontology and a ranked pilot roadmap. There is no commitment beyond the audit.
Keep exploring
The DNA Audit Is Your First Step. And Your Lowest Risk.
In two weeks we map every critical reasoning dependency, broken data link, and governance gap in your current AI or data workflow. You leave with a draft ontology model, a prioritised use case roadmap, and a clear decision on whether - and how - to proceed. No commitment beyond the audit.
Knowledge architecture map
Every logic dependency, data relationship, and constraint your AI needs to reason correctly - visualised and documented.
Gap & risk report
Broken data links, contradictory rules, and governance gaps that are invisible today - surfaced and prioritised by business impact.
Draft ontology & pilot roadmap
A working ontology model for your highest-priority domain, plus a ranked shortlist of pilot use cases with estimated ROI and delivery timeline.
Book Your DNA Audit
2-week engagement · Fixed scope · No ongoing commitment required
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