Endra Says AI Can Produce an Electrical Design in a Day. Who Checks the Work?
Endra says its new Power Studio can complete the electrical design for a 500,000-square-foot commercial building in less than a day. That is a company claim, not an independently measured benchmark. Even so, the more interesting question is not whether the software can work that fast. It is whether an engineer can understand, check and accept what it produces.
The platform is intended to connect engineering calculations, model production and construction documentation in one controlled workflow. Engineers load a building model, define requirements, apply company standards through Playbooks and assign specific tasks to software agents. The stated outputs include Revit models, single-line diagrams, shop drawings, panelboard schedules and bills of materials.
Bringing those pieces together could remove considerable repetitive effort. It also brings the consequences of a bad assumption together.
A coordinated answer can still be wrong
An electrical model may appear complete while carrying the wrong load assumption, code interpretation, equipment selection or routing constraint. A circuit can be connected in the model and still be impractical to install. A family can fit geometrically while failing to represent the approved product.
That is why we would not treat an AI-generated electrical design as a finished design. An experienced engineer should review it for obvious red flags, trace important calculations and correct the design elements that do not fit the project.
The review should include:
governing code and project criteria;
load classifications and demand factors;
voltage-drop and fault-current assumptions;
equipment ratings, clearances and access;
routing, constructability and trade coordination;
alignment with current architectural and structural information; and
consistency across the model, schedules, diagrams and bills of materials.
These are not ceremonial checks. The documents will eventually guide procurement and construction on a live project where people depend on the system working safely.
Human correction should improve the next result
Endra describes engineers reviewing and signing off at each stage. That control point matters. The stronger long-term workflow would also capture why an engineer rejected or changed an AI proposal.
If the engineer moves equipment, changes a calculation method or rejects a route, the system should retain a traceable explanation. Repeated corrections could then strengthen the firm's Playbooks and improve later proposals without turning one person's judgment into an unexplained software rule.
That learning loop needs governance. Teams should know which feedback changed a standard, who approved the change and which projects use the revised rule.
Accountability cannot be automated away
AI can take on repetitive placement, routing and documentation. It can compare information faster than a person working across several disconnected applications. It may even help an experienced engineer see alternatives that would otherwise take too long to study.
None of that transfers professional responsibility to the software.
Before production use, a firm should define:
which tasks the agent may perform;
which inputs and standards it may use;
which decisions require engineer approval;
how changes and calculations will be logged;
how the output will be compared with the previous model; and
who accepts the final design.
Endra Power Studio is an interesting step because it aims at the complete engineering workflow rather than a single drafting shortcut. Its real value will be measured by how clearly it supports engineering judgment, not by how quickly it can generate an apparently finished model.
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