Start with one zone. Link meter, billing, and payment signals; prove recovered revenue on that pocket before scaling.
We have thousands of loss signals a day and no way to work t
You can pilot revenue-assurance analytics on one feeder zone without a full M2C overhaul.
What changes when AI orchestration runs the loop
Not 'more anomaly dashboards' -> 'recover the leakage your AMI flags but no one can chase.'
You have the data and the alarms; the gap is acting on the volume. We work the flagged signals to recovery and learn which patterns are real for your network.
Not 'faster studies' -> 'clear the restudy cycles clogging the queue.'
You've sped the analysis; the coordination and restudy cycles remain serial. A model trained on your study decisions clears clean applications and flags deficiencies up front.
Not 'more asset tracking' -> 'prove the whole program, truthfully, against the current standard.'
You track assets; proving the program (DER, supply-chain, cloud) and producing truthful self-reports stays manual. A model trained on your control history evidences operation and flags drift - owned, auditable.
Not 'more telemetry' -> 'correlate the signals your systems can't, for real root cause.'
You've got SCADA/OMS/GIS/EAM; they don't reconcile in real time. A model trained on your grid data correlates the signals and traces root cause, with the evidence shown.
Not 'more campaigns' -> 'capture the program enrollment your blasts miss.'
You've run campaigns; they're generic. A model trained on your enrollees targets the next likely opt-ins for DER/program uptake.
Where the work changes
Five frames in this vertical's language — leak, operational, governance, analysis, growth.
Leak / value-capture: Not 'more anomaly dashboards' -> 'recover the leakage your AMI flags but no one
We have thousands of loss signals a day and no way to work them without burying the team.
- Meter-to-cash leakage from meter inaccuracies, unmetered accounts, theft, uncollectibles.
- Volume problem shifts from 'do we have data' to 'how do we act on thousands of daily signals.'
- Financial pressure from flat consumption, capex, and regulatory scrutiny on rate increases.
- Loss signals span feeder/DT/consumer levels and must be linked to act.
Operational throughput: Not 'faster studies' -> 'clear the restudy cycles clogging the queue.'
Our interconnection queue is years deep and every deficient application triggers another restudy.
- Interconnection studies are processed largely serially; queues run years deep.
- 90%+ of applications contain deficiencies requiring multiple revision/restudy cycles.
- Coordination across transmission service providers + repeated restudies drive the backlog.
- Study throughput, not demand, is the constraint on connecting new load/generation.
Governance & audit: Not 'more asset tracking' -> 'prove the whole program, truthfully, against the c
CIP is mandatory, audited, and self-reported with million-a-day penalties, and I can't continuously prove my controls hold across an evolving standard.
- NERC CIP is mandatory; FERC enforces via audits, self-certifications, spot checks, and mandatory self-reporting.
- Evolving standard (asset categorization, supply-chain, DER, cloud) makes continuous provability hard.
- Inaccurate data to NERC is itself a separate violation - proof must be truthful and current.
- Cyber insurers require evidence of CIP compliance - weak proof raises cost/blocks coverage.
Analysis / diagnosis: Not 'more telemetry' -> 'correlate the signals your systems can't, for real root
Diagnosing why the grid failed means correlating signals across a dozen systems that don't share asset identity, so root cause stays hidden and crews dispatch with partial visibility.
- Transformer condition in EAM, switching in SCADA, customer impact in CIS, topology in GIS - systems never designed to reconcile in real time.
- Alert/alarm overload during major events; operators manually sort signals without correlation.
- AMI/SCADA/OMS/GIS/DER telemetry don't communicate - can't correlate spikes to feeder stress or asset degradation.
- Billions of data points per utility, but no unified operational truth for root-cause.
Growth / outcome: Not 'more campaigns' -> 'capture the program enrollment your blasts miss.'
Most of our customers are captive; 'growth' for us is program enrollment and C&I account depth, not new logos.
- Regulated/largely captive customer base - classic customer-acquisition growth barely applies.
- Where growth exists: commercial & industrial account relationships, demand-response/DER program enrollment, new-service uptake.
- Enrollment outreach is campaign-bound and under-personalized.
- C&I relationship intelligence is thin.
Where current tooling falls short
Category limitation: large incumbents and AMI platforms surface anomalies and 81% of NA utilities already claim some AI use - but the bottleneck is working the flagged signals to recovery (dispatch, dispute, correct) without overwhelming staff. Surfacing isn't recovering.
What's leaking and what it costs
Frequently asked
Still have questions?
Book a 25-min callCIP is mandatory, audited, and self-reported with million-a-day penalties, and I can't continuously prove my controls hold across an evolving standard. Not 'more asset tracking' -> 'prove the whole program, truthfully, against the current standard.'