Kepler
Kepler is the autonomous operations layer for a continental utilities operator running more than 110,000km of distribution network. It introduces structured machine reasoning into operational decisions previously made under time pressure with incomplete information.
01 — Problem
Operators were absorbing five times the alert volume of a decade prior with the same number of staff. Decisions were getting faster but no smarter; small misjudgements compounded into multi-hour outages.
The brief was not 'replace the operators'. It was 'give them an instrument that thinks alongside them'.
02 — Approach
We composed a reasoning layer that consumed live telemetry, prior incidents, asset histories, and forecasted load. Its job was not to act — its job was to propose, with reasoning attached, and to get out of the way.
The interface was designed for operators under stress: nothing flashy, nothing surprising, every recommendation accompanied by the path of evidence behind it.
03 — System
An event-driven reasoning runtime; a structured model of the network and its assets; a retrieval system over historical incidents; and an interface tuned by long observation of how senior operators actually work.
Every recommendation is tied to its evidence chain. Operators can dismiss any suggestion with one keystroke. The system learns from dismissals.
04 — Outcome
Mean time to diagnose grid-edge faults fell by 47%. Operator-reported confidence (a tracked metric) rose to its highest level in a decade. The system has not replaced a single role; it has made existing roles measurably more capable.
Measured, not asserted.
Time-to-diagnose
−47%
Network covered
110,000 km
Operator confidence
10-yr high