Business Tools
Build an energy management system
Kliv is an AI app builder. Describe your energy management system, and it builds a real app for buildings, meters, interval data, baselines, exceptions, work orders, and budget reports on your own domain.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a utility portal or a generic dashboard — it’s an AI that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the data importers, database, baseline logic, exception lists, work orders, reports, and access rules. You own the result, run it on your own domain, and change it later by asking. It isn’t a fixed reporting template.
On this page, that app is an energy management system for buildings. Kliv builds all kinds of web apps; this is one example.
Why build your own energy management system?
A utility portal shows bills after the fact. A large vendor system can assume a bigger organization than you have. What many facilities teams need is more direct: each building’s usage against what that building should use, on the calendar the building actually follows.
Kliv builds that shape. Interval readings, occupancy calendars, baselines, exceptions, and work orders sit in one app, so waste becomes a dated task with expected savings instead of a suspicion on a bill.
What the meters have been trying to say
Five tables stand between a utility bill and an explanation.
Buildings and meters
Each building has meters, floor area, and systems worth naming, such as the pool heat or gym ventilation. Every reading belongs somewhere specific.
Interval readings
Fifteen-minute readings are kept for years. Waste often appears in the shape of a night or weekend, and those shapes need rows.
The occupancy calendar
Term dates, exam weeks, winter closures, and evening events explain what should have been happening in the building when the energy was used.
Baselines
Each building gets an expected shape by season and day type. A cold January and a broken schedule are not treated as the same thing.
Exceptions and work orders
A persistent deviation becomes a dated task with expected savings attached. The next month’s data shows whether the fix worked.
An example: Casimir’s school district
Here’s how one district might use an energy management system built with Kliv. It’s only an example — you would describe your own buildings, meters, calendars, imports, and work-order rules.
The month’s readings arrive by meter
Casimir imports interval data for nine schools, a pool, and an admin building. Each reading files itself by meter and building.
One building does not match its baseline
Ten buildings look normal. The pool’s overnight line stays flat at 2:00 in the morning for a week, when an empty building should fall toward base load.
The exception points to the visit
The exception names the meter and time window. The site visit finds an air-handling override from a swim-gala weekend that was never reverted.
The work order carries the evidence
The task says to revert the schedule, add an interlock note, and records the expected saving. The graph goes with the work order to the plant room.
The next month proves the fix
The next month’s line drops where it should. The saving appears in the chart and then in the bill, and the exception closes with both attached.
Describe what you want to build
Kliv builds from your description, so the more detail you give, the closer the first version. Name your buildings, meter files, calendars, baseline rules, exception thresholds, and reports. Here are three to build on:
District energy board
Interval imports, baselines, and persistent deviations.
“Build an energy dashboard for our school district with nine schools, a pool, and an admin building. Import 15-minute interval meter data, compare each building to a baseline based on term dates, holidays, exam weeks, and evening events, and list persistent deviations by expected cost.”
After-hours waste finder
Overnight usage checked against base load.
“Build a tool that flags buildings whose overnight electricity stays above base load on empty nights for more than three nights in a week, creates a work order for the head caretaker, and tracks expected savings until the next month’s readings prove the fix.”
Meter data importer
Utility CSVs, sub-meter uploads, and missing feeds.
“Build an importer that accepts our utility’s monthly CSV exports and sub-meter uploads, normalizes readings to 15-minute intervals, files them by meter and building, and reports meters that stopped reporting for more than 24 hours.”
Readings in, reasons out
Data arrives the way your utility provides it. It might be a monthly export, a portal download, or a sub-meter gateway. One readable function makes it uniform, and bad feeds are logged instead of silently smoothed.
The work is scheduled. Baselines compare overnight, each head caretaker gets a weekly digest for their building, and the district summary is compiled for the monthly budget meeting. Kilowatt-hours and money come from the same rows.
Access follows responsibility. A caretaker sees their building’s detail. The district efficiency table can show every building’s headline index. The board reads totals. Row-level rules keep the detail scoped.
The meters have been keeping records for years. Internal tools that finally read the record are a common Kliv use case, and this one can repay the build within a season.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the energy management system you need, it builds the app, and you own the result. For this page, that means meter imports, baselines, exceptions, work orders, and reports.
Do I get a real app, or a template?
A real app. Kliv builds the database, import logic, screens, scheduled jobs, reports, and access rules for your buildings and meters.
Can I change it after it’s built?
Yes. Add a meter type, change a baseline rule, add a report, or adjust exception thresholds by asking in plain words.
Where does the meter data come from?
From what you already have: utility CSV exports, portal downloads, or sub-meters posting through a gateway. Kliv builds an importer for your source.
Will more meters make Kliv cost more?
No. Kliv does not charge by the number of meters in your app. Eleven buildings or 40 are rows in your database.
Can caretakers see each other’s buildings?
Only if your rule allows it. A caretaker can see their building’s detail, while a shared table can show headline indexes across buildings.
What about gas and water?
You can add another meter type by asking. The schema changes, and the same baseline and exception approach can apply.
Can it create work orders?
Yes. A persistent deviation can create a task with the building, meter, time window, graph, expected saving, and owner.
Can it report savings to the budget meeting?
Yes. Reports can read the same rows used for readings, exceptions, and completed work orders, so the meeting number matches the database.
Can it handle missing meter data?
Yes. Importers can report meters that stopped reporting or feeds that arrive malformed, instead of hiding the gap.
Can I use my own domain?
Yes. The app runs on your own domain with the roles and access rules you define.
Can I move off Kliv later?
Yes. The code syncs to your own Git repository, so leaving is a git pull, not a rebuild.
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Find what an empty building is using
Describe your buildings, meters, calendars, imports, baselines, exception rules, and reports in detail. Kliv builds the energy management system around them.