From pv magazine Germany
German startup Invertix has developed a suite of 22 AI “workers” designed to independently manage operation and maintenance (O&M) functions for solar power plant operators.
“In contrast to general-purpose agents like Open Claw,” CEO and co-founder Joseph Perrotta told pv magazine, “Invertix’s agents are highly specialized for solar and renewables, understanding inverter fault patterns, Modbus registers, and act autonomously within operational boundaries. Critical decisions are escalated to humans, similar to an experienced technician working independently but consulting on major issues.”
Perrotta and co-founder Kaan Durmaz said their experience with large language models and autonomous AI agents showed that these systems can handle complex operational processes. They conducted more than 500 discovery calls with asset managers in Milan, O&M managers in southern Spain, energy traders in Scandinavia, and CEOs of independent power producers (IPPs) in Germany. They also analyzed SCADA systems, maintenance schedules, regulatory reports, and network operations to identify tasks that do not scale efficiently.
Perrotta said Invertix’s clients range from large utilities to specialized O&M providers, asset managers, and project developers, all facing similar operational bottlenecks. The company focused on three segments with acute challenges, particularly IPPs and asset managers managing portfolios ranging from hundreds of megawatts to gigawatts across solar, wind, and storage assets.
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“Although these companies are expanding faster than ever, the underlying processes remain largely unchanged, relying on manual workflows, siloed data, and fragmented tools,” Perrotta said. “Teams are not too small; processes are simply too slow, limiting growth.”
The company said its discovery calls identified several recurring challenges.
Modern SCADA systems, for example, generate thousands of data points per minute per installation. When combined with weather, market, grid, contractual, and regulatory data across disconnected systems, this creates significant complexity. Existing tools are often unable to process this volume effectively, leaving potential insights underutilized.
The analysis also showed of the daily workload is dominated by manual, low-value tasks, including exporting SCADA data to spreadsheets, compiling performance reports, classifying alarms, completing regulatory forms, checking invoices, and reviewing power purchase agreement (PPA) clauses. In some cases, staff spend hours each morning manually classifying hundreds of SCADA alarms, delaying corrective actions.
Furthermore, the calls revealed that PV plant operators also face constraints when expanding portfolios. While companies aim to deploy new projects, existing workloads limit capacity. The issue is not workforce size, but processes that scale linearly with asset growth.
In addition, regulatory complexity and national reporting requirements were found to place further strain on teams, diverting resources toward compliance and increasing the risk of errors.
Perrotta said existing tools do not fully address these issues. SCADA and monitoring platforms, for exemple, primarily visualize data without taking action, while asset management software still requires manual interpretation. Generic AI tools lack domain-specific capabilities, such as handling Modbus registers, OPC-UA configurations, or distinguishing between PPA-related curtailment and grid events. Consulting projects, he added, typically deliver reports rather than autonomous solutions.
“We have built a full team of 22 specialized AI workers across seven core departments, enabling portfolio managers to oversee significantly larger capacities than would otherwise be possible. For example, in technical asset management, three AI workers handle SCADA monitoring, alarm and event analysis, and grid analysis,” Perrotta stressed.
According to the company, the SCADA monitoring agent processes thousands of data points per minute, while the alarm and event analysis agent classifies alarms in real time. A grid analysis agent correlates grid frequency with plant performance to identify network issues before outages occur. Each AI worker contributes data that continuously improves the models.
Costs range from €2,000 ($2,297) to €4,000 per month per AI worker, plus departmental fees, according to Perrotta. “Invertix is responsible for software functionality, while clients retain final decision-making authority,” he said. “Actions follow a three-stage model – inform, recommend, act – with human intervention required for high-risk decisions.”
Perrotta added that conventional monitoring systems rely on static thresholds, often producing false alarms. In contrast, Invertix’s AI workers evaluate alarms using SCADA telemetry, historical patterns, weather data, grid conditions, and neighboring plant performance, prioritizing relevant issues and providing contextual analysis.
He also aknowledged that the proposed approach has limitations. AI workers cannot perform physical tasks such as replacing inverters or conducting on-site inspections, nor can they manage strategic negotiations or investor relationships. “The goal is to free humans from repetitive work to focus on higher-value activities,” he said.
Although Invertix was officially founded in February 2026, the company said it began working with live clients within three weeks. Pilot projects include a large O&M provider in Italy managing 1.5 GW of solar assets and an Italian IPP managing 350 MW. The AI workers are used for SCADA monitoring, alarm classification, portfolio performance analysis, automated reporting, and identifying optimization opportunities.
The company plans to invest new capital in product development, expand its AI worker portfolio, and further train models using client data. It is targeting the DACH region and southern Europe, citing increasing demand for automation in renewable energy operations.
Invertix is also inviting renewable energy companies to submit SCADA data from underperforming assets for free analysis. The company said it aims to identify inefficiencies and gather industry insights to guide future development.
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