Private AI for utilities.

When people picture AI at a utility, they jump straight to the grid — control systems, outage prediction, critical infrastructure. That's the hardest, most regulated corner of the problem, and it's not where most utilities should start. The faster, lower-risk wins are on the customer-facing side: communications, outreach, and the constant flow of program and education content. The reason to do that work with private AI rather than a public service is simple — it touches customer data, and in California that data is legally fenced in.

The customer-facing work AI is good at

• Customer communications — drafting outage notices, billing explanations, and service updates in clear, consistent, plain language.
• Multilingual outreach — producing the same notice across the languages a service territory actually speaks.
• Efficiency and rebate programs — turning dense program rules into customer-friendly descriptions, eligibility explainers, and FAQs.
• Public relations and community content — drafting announcements, newsletter copy, and board-meeting summaries.
• Internal knowledge — a field-service or policy knowledge base that answers staff questions from your own manuals.

Most of this is writing and retrieval — exactly what today's models do well, and none of it requires touching the control network.

Why customer data is the deciding factor

Utilities operate under customer-data rules most industries don't, and they're the reason private AI is the right default for this work. As always, none of these laws mandate private AI — but they make it the path of least resistance:

• California limits sharing customer usage data with third parties. Public Utilities Code § 8380 prohibits electrical and gas corporations from disclosing or making customer "consumption data" — usage plus name, account number, and address — accessible to a third party except in narrow circumstances or with the customer's consent, and bars selling it outright. Piping customer data into a public-cloud AI vendor is exactly the kind of third-party disclosure the statute is written to constrain.
• The CPUC built privacy rules around customer data. The Commission's privacy decision for smart-meter usage data (D.11-07-056) adopted Fair Information Practice Principles — data minimization, use limitation, security — that bind not just the large investor-owned utilities but their contractors and any third party that gains access, and it requires specific, per-purpose consent before customer data goes to an outside party.
• The broader privacy regime adds exposure. California's general privacy law carries statutory damages for breaches that result from a failure to maintain "reasonable security." (Its applicability to regulated utilities is more nuanced than for ordinary businesses, so treat this as added exposure rather than the primary rule.)

The common thread is disclosure and consent. Every time customer data leaves your systems for an outside service, you take on consent mechanics, contractual security requirements, and disclosure risk. Processing it in-house removes the trip entirely — the data never becomes "accessible to a third party," because there's no third party.

What this looks like in practice

A private setup for utility customer-facing work usually means a model running on hardware the utility controls — an in-house inference server or a private cloud — with an access and logging layer in front of it so administrators can see who used what and demonstrate that customer data stayed inside the perimeter. The Interchange AI Gateway provides that policy-and-audit layer when you need to show, not just assert, where the data went.

Where to start

The honest starting point for most utilities is a narrow, customer-facing pilot — pick one content workflow, run it privately, and measure it — rather than a grid-scale program. We can help scope that, and you can see more of what we do for utilities specifically.