Every other demo on this site is for the people who run the grid. This one is for the people who pay for it. Follow one billing month at the Rivera household (fictional), EV, electric water heater, pool pump, two working parents on a time-of-use rate they never chose to understand. The advisor reads their actual smart-meter shape, moves the flexible loads to cheap hours without touching their comfort, warns them mid-month before the bill becomes a surprise, and turns a grid emergency into a $24 credit instead of a blackout story. The bill drops $92. The trust goes up more. And the utility gets a customer who leans in, 40,000 of whom just became a 34 MW virtual peaker.
| Without | With Advisor | Δ |
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| Without | With Advisor | Δ |
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This demo follows one 30-day billing month at the fictional Rivera household: two working parents with an electric-vehicle (EV) charger, an electric water heater, a pool pump, a dishwasher, and air conditioning. They are on a "time-of-use" rate, meaning the price of electricity changes by the hour: 9 cents per kilowatt-hour overnight, 16 cents midday, and 34 cents during the 4-to-8 PM evening peak. They never chose to understand any of that, which is normal, and last month's bill was $231. The screen shows their house, a 24-hour schedule laid over the cheap and expensive hours, and a running "bill race": what the month costs if they do nothing (red) versus if they follow the advisor (green). The feed is their phone. Every suggestion is opt-in: one tap to accept, one tap to say no.
Three suggestions arrive during the month, and each waits for the family. Day 2, the smart schedule: the advisor reads the home's smart-meter data, notices the car charging at 6 PM, the single most expensive hour of the day, and the pool pump running all afternoon. It proposes charging the car after 11 PM, running the pump overnight, heating water before the 6:30 AM showers, and delaying the dishwasher an hour: worth about $55 this month, with nothing changing but the clock. Day 6, it trims the pool pump from 6 hours to the 4 the water actually needs, another $4. A day-11 check-in shows the bill heading to $170, which is $61 below last month, so there will be no surprise at month end. Day 15, the heat-wave plan: six days of 101-degree weather are coming, on track to add $19 to the bill. The plan cools the house to 71 degrees between 1 and 4 PM, while electricity is still cheap, then lets it drift up to 75 through the expensive evening hours. The stored coolness carries them through, saving about $14 of that $19, and their usage in the expensive window drops 48% with no one feeling a difference. Day 20, the conservation event: the grid is short on power for tomorrow evening, and the utility offers a $2 credit for every kilowatt-hour a home shifts out of the 5-to-8 PM window. The advisor proposes pausing the car charger, delaying the water heater, and pre-cooling at 2 PM. The Riveras tap "I'm in," feel nothing, and earn $24.20. Their neighborhood's peak demand drops 11%, and the 40,000 enrolled homes together free up 34 megawatts, the output of a small power plant, without building one.
The month closes at $153: $92 less than doing nothing, including the $24 event credit. Along the way the advisor also flags an odd usage pattern in the garage as a possibly failing freezer, the kind of catch that saves a customer hundreds. If the family ignores every suggestion, the demo plays the usual version instead: the car keeps charging at the most expensive hour, the heat wave runs straight through peak prices, the conservation-event email joins the 91% that go unread, and the bill lands as a $245 surprise, higher than last month on the supposedly money-saving rate. Then come the 40 minutes on hold, the 1-star app review, and the search for rooftop solar. The closing tiles: this month's bill $153 versus $245, saved versus doing nothing $92, comfort changes felt none. The dollar figures are recomputed live from the bill race on each run; the values here are the month the feed narrates.
The failure is translation. The utility rolled out an hourly-priced rate and handed the family a brochure. Nobody notices the car charging in the most expensive hour, because noticing is nobody's job. The mid-month drift toward $229, before the heat wave even hits, is invisible until the bill arrives. The conservation event is a mass email that gets 9% participation and the Riveras never see it, so the utility fires up its expensive backup power plant instead. The month ends at $245, which is $13 higher than last month on the "savings" rate, and the bill itself damages the relationship: the hold queue, the 1-star review, the solar quote from a competitor.
The Personalized Energy Advisor reads the home's smart-meter data, works out which appliance is using what, and prices every scheduling choice against the actual rate. It proposes; it never imposes. The family approves all three suggestions and can override any of them on any day. The car charges at 9 cents instead of 34, the house banks cheap cooling ahead of each hot evening, and the grid emergency becomes a $24 credit instead of an outage story. The bill closes at $153 with zero comfort changes felt. On the utility side, 40,000 households like this one add up to 34 megawatts of relief on the worst evenings, delivered by participation instead of by building a new plant.
| KPI | Without Advisor | With Advisor | Delta |
|---|---|---|---|
| The billwhat the Riveras owe for the month | $245 | $153 | $92 kept |
| Annualized savingsthe monthly saving projected across a full year | none | ~$938 | real money |
| EV charging pricethe electricity price the car charges at | 34¢/kWh at 6 PM | 9¢ overnight | 74% cheaper per charge |
| Heat-wave surchargewhat six days of 101-degree weather add to the bill | ~$19 | ~$6 | cheap cooling banked ahead of the peak |
| Event daywhat the grid-emergency day meant for this family | never saw the email | earned $24 | trust built |
| Comfort changes feltwhether anyone in the house noticed anything different | n/a | zero | same life, cheaper |
| Bill surprisewhether the month-end number was a shock or a known quantity | $13 HIGHER on the "savings" rate | known by Day 11 | no shock |
| Peak-event capacity (× 40,000 homes)the power freed up when every enrolled home shifts together during the emergency | 9% email response | 34 MW dispatched | a power plant made of goodwill |
| Bill-shock contact callsangry calls to the call center after surprise bills, each costing the utility money to handle | 1 per surprise × thousands | deflected by forecast | dollars saved per call avoided |
| TOU rate perceptionTOU means time-of-use, the rate where prices change by hour; this row is what customers come to believe about it | "a trap" | "a savings machine" | the rate gets accepted |
| App rating trajectoryhow customers rate the utility's app, a proxy for the relationship | 1-star reviews | daily-use utility app | customers engage |
| Churn/defection signalearly signs a customer is looking for a way around the utility | googling rooftop solar | leaning into programs | customers kept |
| Feeder peak (advisor homes)peak demand on the neighborhood power line; lower peaks delay expensive upgrades | baseline | −11% on event day | wire upgrades deferred |
| Consumer-protection auditwhether the utility can prove every nudge it sent was fair and in the customer's interest | ad-hoc | every suggestion logged with its reasoning | defensible to regulators |
The safety effect is indirect. A VPP that actually delivers what it promised on a peak day reduces the emergency operations that follow when it does not: manual load transfers, callouts, and field staff working a hot afternoon into the night.
Counted in units you already track:
Event day hours come back to the program director and the monthly reporting week comes back to the program analyst.
The numbers we need from you to run that formula:
| Cost driver | How it is calculated, from a rate you supply |
|---|---|
| Capacity value delivered | incremental megawatts delivered against commitment x your capacity price or your own avoided capacity cost |
| Program labor | event and reporting hours avoided x your loaded program staff rate |
| Underdelivery exposure | your penalty or shortfall charge per megawatt x the shortfall megawatts you currently incur in a typical season |
| Market revenue | megawatt hours bid into energy or ancillary products x the settled price in your market, for the hours the portfolio was previously idle |
| Incentive efficiency | your incentive payment per enrolled device x the devices you no longer need to call because the dispatch is better ordered |
You pay for the orchestration platform, for an integration to each vendor DERMS dispatch interface, and for the contract work to get the vendors to expose those interfaces at all, which is often the slow part. Your program staff also need time to validate the combined forecast against a season of real events before they will offer against it.
Payback is usually dominated by the capacity value of delivering the commitment plus the penalty exposure you stop carrying. Program labor is real but it is the smaller line.
There is no direct safety benefit here and we will not manufacture one. This is desk analytics. The honest mechanism runs through arrears: a customer on a rate that fits their usage, with a plan they understand, is less likely to fall behind, and what falling behind produces is field collection and disconnect visits, which are among the highest confrontation and assault risk activities a utility asks of its people.
Counted in units you already track:
Advisor and analyst hours come back, and the advisor stops rebuilding bills and starts having the conversation.
The numbers we need from you to run that formula:
| Cost driver | How it is calculated, from a rate you supply |
|---|---|
| Advisory labor | manual rate comparisons avoided x hours each x your loaded advisor rate |
| High bill contact volume | rate and high bill contacts avoided x your average handle time x your loaded representative rate |
| Program marketing efficiency | your current cost per enrollment x the enrollments you would otherwise have bought with untargeted mail, using your own take rates on targeted versus untargeted lists |
| Arrears and bad debt | your own write off rate x the balance that no longer ages, using the share of high bill arrears you believe a rate switch would have prevented, which you set |
| Program administration | analyst hours avoided on list building and post campaign reconciliation x your loaded analyst rate |
You pay for the GridCORTEX analysis service, for integration into your meter data management system, your customer information system, and your existing email and portal delivery channels, and for your own tariff analyst time to validate the rate engine against a sample of real issued bills before anything is sent. That validation is not optional and it is the largest internal cost in year one.
Payback is usually carried by advisor hours and by high bill contact volume, not by program enrollment lift, because enrollment lift depends on your offers and your channels rather than on the analysis. Build the case on the two you can count and treat program lift as upside.
The safety effect is indirect and we will say so plainly. A chatbot does not keep anyone off a pole. The real mechanism is two steps down: duplicate and already restored outage reports generate trouble dispatches that put a truck on the road for nothing, and a downed wire report captured with a clean location and hazard description reaches dispatch faster, which shortens the time a live conductor sits on the ground with the public near it.
Counted in units you already track:
Average handle time and after call work come back to the contact center, and the supervisor stops being the human broadcast system for restoration estimates.
The numbers we need from you to run that formula:
| Cost driver | How it is calculated, from a rate you supply |
|---|---|
| Contact center labor | contained contacts x your average handle time x your loaded representative rate |
| Overflow and after hours vendor | contacts contained outside business hours x your contracted per minute or per contact rate |
| Supervisor broadcast work | estimate changes per year x minutes to republish across channels x your loaded supervisor rate |
| Avoided trouble dispatch | duplicate or already restored tickets avoided x your fully loaded cost per truck roll, a figure your dispatch group already carries |
| Complaint handling | escalations and regulatory complaints avoided x your loaded hours per complaint case, using your own share of complaints that trace to a wrong restoration estimate |
You pay for the GridCORTEX conversational service, for read integrations into the outage management system, the customer information system, and your contact center platform, and for your own staff time to write and approve the answer set and review transcripts during the pilot. Transcript review is the part utilities underestimate, and it is heaviest through the first two storms.
Payback is contained contacts times average handle time times your representative rate, and it concentrates in storm months. Build the case on contained volume you can count in your own contact center reporting and treat avoided trouble dispatch as upside your dispatch group has to confirm.
This is back office work, so the safety mechanism is one step removed and specific. Unresolved exceptions generate check read and meter verification truck rolls, and unresolved exceptions turn into arrears, and arrears turn into field collection and disconnect visits. Field collection and disconnect visits are among the highest assault and confrontation risk activities a utility asks of its people, and every one avoided is also road miles not driven.
Counted in units you already track:
Billing analyst hours come back, the queue stops aging, and the analyst moves from tracing to deciding on the cases that genuinely need a person.
The numbers we need from you to run that formula:
| Cost driver | How it is calculated, from a rate you supply |
|---|---|
| Billing operations labor | exception and rebill minutes avoided x your loaded billing analyst rate |
| Contact center | exception driven contacts avoided x your average handle time x your loaded representative rate |
| Field verification | check read and meter verification truck rolls avoided x your fully loaded cost per truck roll |
| Unbilled revenue carry | average unbilled balance sitting in the exception queue x the days the queue is shortened x your weighted average cost of capital expressed daily |
| Bad debt | your own write off rate x the balance that no longer ages behind an unresolved bill, using the share you attribute to billing disputes |
You pay for the GridCORTEX resolution service, for integration to the customer information system adjustment interface and your meter data management system, and for your own billing controls staff to define which corrections may post automatically, which hold for a human, and where the high balance threshold sits. Expect a genuine internal effort to agree those thresholds before go live.
Payback is dominated by analyst minutes per exception times your exception volume, with contact center savings second. Unbilled revenue carry and bad debt are real but they are the lines your finance group will challenge, so carry them as upside.
What is this, exactly? It is AI software: intelligent agents and models built and delivered by SoftServe, running on NVIDIA accelerated computing. It is not a hardware appliance and it does not replace the systems you run today. It deploys in your own cloud or on your premises, connects read-only to your existing systems, and recommends; your people approve every action, starting in shadow mode until it earns trust.
An analysis and recommendation service for customer programs teams that turns each customer's interval data into a personal energy plan: rate comparison, savings actions, and matched program offers, delivered through your existing channels. The demo above uses synthetic data; everything below describes what the real deployment needs from your organization.
| Your system | Typical products | How we connect |
|---|---|---|
| Metering (AMI head-end and meter data management) | Itron, Landis+Gyr; Oracle or Itron meter data systems | database replica refreshed nightly |
| Customer Information System (CIS) / billing | Oracle CC&B, SAP IS-U | database replica refreshed nightly |
| DER management (DERMS) and DER program platforms | Uplight, EnergyHub, Virtual Peaker | read-only API |
| Document and knowledge stores | rate tariff library, program catalogs | document upload |
| Outage Management System (OMS) | GE PowerOn, Oracle NMS, ADMS outage module | read-only API |
| Asset / work management (EAM/CMMS) | IBM Maximo, SAP PM | database replica refreshed nightly |
| Market and grid operator interfaces | PJM, MISO, CAISO portals; settlements | read-only API |
Runs in your cloud account on GPU instances; customer interval and billing data stay inside your environment under your privacy rules with need-to-know access, and all connections are read-only. Recommendations flow through your existing channels, and the pilot runs against a control group.
The Approve button you just clicked in the demo above is the real workflow. This is what it looks like on the screen of the customer programs manager in the GridCORTEX console:
Approve queues the plan batch in your existing email and portal delivery channels tied to the CIS, in pending status until your marketing rules and suppression lists run; GridCORTEX never emails customers directly. Your team can pull any customer from the batch before send.
Interval data, rates, and program catalogs flow in automatically; the manager only sets the target segment and send window when creating a batch.
Uses each customer's latest 12 months of AMI interval data, refreshed daily, and current tariffs; each batch card shows the data as-of date.
Customers see plans in the portal; the team works in the GridCORTEX console; email push when a batch is ready. The console runs in a browser beside your existing screens on day one; embedding into your own systems is a roadmap step once the read-only phase has earned trust. Approve, Modify, and Decline are all captured in an audit trail your compliance team can pull, and GridCORTEX never blocks or overrides anything in the systems you run today.
The fair question from any Chief Customer Officer: "We have a mobile app, energy-use tips on the portal, and a demand response program, what's new here?" Here's the honest answer.
When someone asks "what did it actually calculate?", this is the list. In the simulation these factors drive the storyline; in a pilot they are computed from your rate book, AMI data, and program catalog.
Presenter's one-liner: "The advisor read one family's meter, moved their EV and pool pump to nine-cent hours, warned them before the heat wave hit their bill, and turned a grid emergency into a twenty-four-dollar credit. They saved $92 and felt nothing. Multiply by forty thousand homes and the utility got a 34-megawatt peaker made entirely of goodwill."