AI systems for energy and utilities.
The workflow pattern we see in this industry, and what we would build for it.
01
Contact arrives
A customer calls about a bill or an outage. An agent navigates several screens to find the answer.
02
Answer read back
Most contacts are routine. Each one still takes a person, a queue, and a hold time.
03
Work order typed
Field work is created by hand from the call notes. Skill, parts, and duration are guessed.
04
Truck rolls
The tech arrives without the right context or truck stock. Return trips absorb the day.
A service and work-order agent.
An agent that resolves routine billing and outage contacts from live system data, drafts work orders with predicted skill and parts, and escalates the complex cases to people. Your service center keeps the final say on every escalation. The agent removes the queue, not the human backstop.
10 to 14 weeks from kickoff to a live production system. We stay through adoption.
Eval harness
Checks agent outputs against a test set before release.
Reliability gates
Hard checks the system must pass before it ships.
Live production system
The working system in your stack, not a report.
Adoption Mile
To the 70 percent weekly-active bar by day 90.
Customer contacts that are routine
About 60 percent or more
IllustrativeCommercial building energy waste considered addressable
20 to 30 percent
IllustrativeFirst-time-fix lift from better work orders
Varies by territory
Illustrative
Ranges are industry observations for orientation. They are not ClearForge results. Our results live on the proof page.
See the proofStart where every engagement starts. The fixed-fee Diagnostic maps this workflow in your service center in 2 weeks.
Starting point · Forge Diagnostic