Data scattered across systems
SCADA, inverter portals, charger back-ends, meters and spreadsheets all report differently, so every report starts with a manual stitch.
Industries / Clean Tech
Agentic BI for Clean TechYour energy data has a goal.It finds why output falls short.
The agentic BI platform for clean tech teams. Your AI analyst investigates generation, downtime and emissions across your SCADA, charging and metering data. Every number cited, never guessed.
No credit card · 14-day team trial
Clean Tech analysissite_output.csv
LiveQuestion
Why is output below forecast this month?
Output vs forecast, by site%
Root cause
Three inverters at Dune East keep tripping offline, and they account for 71% of the site’s shortfall.
60s
to connect your database and start asking
100%
of numbers cited to a query that ran
24/7
monitoring on the assets and metrics you put on watch
The clean tech data problem
Every asset reports. Almost nothing is explained.
SCADA, inverters, chargers, meters and weather feeds each tell part of the story. Seeing that output dipped is easy. Knowing why takes an engineer days, and the lost generation can’t be recovered.
Output below expectation
Generation falls short of forecast. Is it soiling, shading, a failing inverter or just weather? Nobody can say quickly.
Downtime found too late
Faults and offline assets surface in the monthly report, after the revenue is already gone.
Storage and chargers that don’t pay
Batteries cycle at the wrong times and chargers sit idle, and it’s hard to see what each one really earns.
Emissions reporting by hand
Carbon and energy figures are rebuilt from spreadsheets every quarter, and the numbers rarely tie out.
Dashboards that go stale
Static exports are out of date the moment they’re emailed, and two teams rarely agree on the same KPI.
How it works
From connected to monitoring in two days
No SQL, no ticket to the data team. SchemaChat does the heavy lifting.
See it in action
Ask the questions asset and operations teams actually ask
Four real-world questions, answered the way SchemaChat answers them: the number, the why, and a chart of what it found.
Ask
Why is solar output 6% below forecast this month?
- Compared actual output with forecast by site, array and hour
- Isolated Dune East and its inverters
- Separated weather effects from equipment losses
SchemaChat
Output is 6.2% below forecast, a shortfall of about 410 MWh. Dune East accounts for most of it.
Root cause
Three inverters trip offline most afternoons, and soiling on the lower rows adds a further 3% loss.
Ask
Which assets lost the most uptime this quarter?
- Calculated availability by asset, site and fault code
- Compared each asset against its own 12-month baseline
- Matched outages to maintenance and weather logs
SchemaChat
Fleet availability is 96.1%, down from 98.0%. Wind turbines T3 and T9 account for 54% of the lost hours.
Root cause
Both turbines wait weeks for the same replacement gearbox sensor, so short faults turn into long outages.
Ask
Why are our chargers earning less per day?
- Compared revenue, sessions and utilisation by site and charger type
- Split sessions into completed, failed and idle-blocked
- Checked the fault history of the lowest earners
SchemaChat
Revenue per charger fell 11% while sessions held steady. Failed sessions at fast chargers explain most of it.
Root cause
One in six fast-charger sessions fails at start-up, and a firmware fault at three sites drives most of them.
Ask
Why did avoided emissions miss target this quarter?
- Converted generation and dispatch into avoided emissions
- Compared each site and asset against its target
- Traced the gap to output, dispatch and curtailment
SchemaChat
Avoided emissions were 9% below target, about 1,800 tCO₂e. Low wind and an idle battery explain most of it.
Root cause
The harbour battery sat idle through 40 high-emission evening hours, and wind output ran 12% below its seasonal average.
What teams use it for
One analyst for every clean tech question
From the control room to the sustainability report, the same governed numbers answer every team.
Output · Capacity factor · Perf. ratio
Generation & output analytics
See how much each site, array and turbine produces against forecast, and what explains the gap.
Availability · Fault codes · Repair time
Asset uptime & maintenance
Catch faults, track availability and schedule maintenance before small issues become long outages.
Utilisation · Failed sessions · Revenue
EV charging networks
Understand utilisation, failed sessions and revenue by site and charger, and fix the weak spots.
State of charge · Cycles · Revenue
Battery storage & dispatch
See when batteries charge and discharge, and whether each cycle earns what it should.
kWh / m² · Peak demand · Baseload
Energy efficiency & demand
Find where buildings and sites waste energy, from baseload to peak, and which savings are real.
Avoided CO₂e · Scope 2 · Intensity
Emissions & ESG reporting
Turn energy and activity data into emissions figures that tie out, ready for reporting.
Automated dashboards
Operations dashboards that build themselves
- Ask “build me a renewable assets dashboard”. It picks the KPIs and charts.
- Live, not snapshots. Every widget re-queries on each view.
- Click any site on the map to cross-filter the whole board.
Try it: click a site on the map.
Build me a renewable assets dashboard
Output94.1%−1.9pt
Availability96.1%−1.9pt
Capacity factor27.8%+0.6pt
Curtailment3.1%+0.4pt
Sites & grid linksColour = output vs forecast
Output vs forecast by site
Output vs forecast, last 12 weeksNow 94.1% · was 98.2%
Integrations
Works with the data you already collect
SchemaChat reads the database your energy systems already feed. There’s no new tracking to install and no data to re-enter.
- SCADA & asset telemetry
- Inverters & turbines
- EV charger back-ends
- Battery management
- Meters & billing
- Weather & forecasts
- Maintenance logs
- Spreadsheets & CSV
Your database read-only
SchemaChat reads from
- PostgreSQL
- MySQL
- BigQuery
- Snowflake
- MongoDB
- CSV files
schemachatAI data analyst
- Root-cause analysisInvestigating output shortfall
- Live dashboardsRenewable portfolio · re-querying
- Alerts & monitoringAvailability < 95% · watching
- Cited answersEvery number traced to its source
Read-only, credentials encrypted.See all integrations →
FAQ
Questions clean tech teams ask
Which clean tech systems does SchemaChat work with?
SchemaChat connects to the database your systems write into: PostgreSQL, MySQL, BigQuery, Snowflake or MongoDB. If your SCADA, inverter, charger or metering data lands in one of those, or in a CSV export, you can start asking questions straight away.
Do engineers or analysts need SQL knowledge?
No. You ask in plain English. SchemaChat writes and runs the queries, and shows the exact SQL behind every answer, so your data team can check it any time.
Can it combine data from different assets and systems?
Yes, if the tables are in the same connected database, SchemaChat can join them. For example it can link inverter output to weather readings and maintenance logs by site and time, and define the join once as a governed metric.
How do I know the numbers are right?
Every figure comes from a query that actually ran, with the step behind it cited. If the data isn’t there to answer a question, SchemaChat says so rather than guessing.
Is our operational data safe?
All AI queries are SELECT-only, enforced at the SQL parser layer, so SchemaChat cannot modify or delete your data. Database credentials are encrypted at rest, and you choose which tables and views it can see.
Can it handle high-frequency sensor data?
SchemaChat runs its queries against your database, so aggregated tables, for example 5-minute or hourly readings, are the best fit. Very high-frequency raw streams are better rolled up first.
Can it alert us when an asset underperforms?
Yes. Put any finding on watch, for example a site’s output against forecast or an asset’s availability, and you’ll be alerted when it crosses a threshold, along with a digest of what changed.
Can we pull answers into our own tools?
Yes. The REST API lets you ask questions, run read-only queries and pull charts from your own backends, Slack bots and automations using a Bearer token.
How much does it cost to try?
The 14-day team trial lets you connect your own database (read-only) or upload CSVs, run deep analyses and build dashboards, with no credit card and up to 3 seats. After the trial, Premium and custom plans for larger teams are on the pricing page.
Put an AI analyst on your clean tech data today
Connect a database or upload a CSV of recent readings and ask your first question in about a minute.
- 14-day team trial, no credit card
- No credit card
- Cancel anytime