Skip to content
GAIAA, Intelligence, engineered
Solutions
Solutions overviewBuild, deploy and improve AI systems that deliver real value.Private AI infrastructureRun private AI securely in your infrastructure.Existing AI optimizationMake existing AI faster, more reliable and more valuable.
Main specializations
Computer VisionSmart CityLIMSIndustrial IoT
IndustriesCase StudiesAbout usBlogContact
ENPL
Solutions
Solutions overviewPrivate AI infrastructureExisting AI optimization
Main specializations
Computer VisionSmart CityLIMSIndustrial IoT
IndustriesCase StudiesAbout usBlogContact
ENPL
GAIAA, Intelligence, engineered

Your signals, systems and constraints become the starting point for AI that can work in the real world.

Solutions overviewIndustriesCase StudiesAbout usBlogContact

Bring a process, signal or recurring decision.

Start the conversation
GAIAA sp. z o.o. sp.k.
ul. Sofoklesa 17
60-461 Poznań, Poland
NIP: 7811993317
KRS: 0000777888
© 2026 GAIAAPrivacy policyhello@gaiaa.eu+48 666 308 008LinkedIn
ENPL

Case study: Energy

When the API did not exist, the interface became the data source.

Existing SCADA screens became a reliable data source without replacing the system or waiting for an API.

SystemOutcomesRelated workDiscuss your case

System

The project and the problem

A renewable-energy operator used proprietary supervisory control and data acquisition (SCADA) software to monitor turbines and operating conditions across multiple wind farms. Staff could reach each system only through a restricted remote-desktop screen: there was no usable API or database connection. The screen itself was therefore the only available data source, but conventional optical character recognition was unreliable because the interface rendered heavily antialiased vector text with ambiguous character edges.

How the process worked

Automation navigated each remote session and captured the relevant screens. Vision models first located tables, rows, columns, labels, status graphics and value regions; difficult text was then split into individual glyphs, classified and ordered back into words and numbers before the reconstructed record was structurally validated.

From screen to record

Watch a locked screen become usable data.

Use the controls below to inspect the key stages, constraints and validation decisions in that process.

From operator pixels to a governed record

Follow one scheduled capture through detection, reading and validation.

Source
Restricted RDP
Cadence
5 seconds
Captured frameWF-A / Turbine T-14

CAP-14321014:32:10T14-overview v3.2

Read values

Output: 1856 kW

Exact pixel crop
1100%8100%5100%6100%
Primary reading
1856 kW
Confidence
100%
Scheduled captures3 of 5
Extraction history3 records observed
TimeOutputTemperatureStateAlarmDestination
14:32:00Not availableNot availableNot availableNot availableReading pixels
14:32:05Not availableNot availableNot availableNot availableReading pixels
14:32:10Not availableNot availableNot availableNot availableReading pixels

Provenance · RDP-OPS-04 / T-14 overview · T14-overview v3.2 · vision-edge 2.4

Outcomes

Operating constraints

  1. No usable API or database access
  2. Restricted desktops and permissions
  3. Antialiased interface text
  4. Multiple visual structures and alarm states

What changed?

  1. Automated observation
  2. Structured historical data
  3. Alert-ready events
  4. Less dependence on continual manual viewing

Evidence retained: Each structured record retained its source screen, extraction state and validation outcome for review.

Related work

SolutionIndustrial IoT

Discuss your case

Have a similar operating problem? Share the environment, constraint and result you need. We can discuss the engineering path without exposing confidential details.

Discuss a similar challenge