The AI that knows your transformers
before they fail
RONIN AI is the simplest reliability health assessment system for power transformer built for utilities, grid operators, and asset managers who can no longer afford to wait for failures.
Trusted by asset managers and grid experts
4.9
From 500 kVA to 800 MVA
Monitorable Capacity
0
Accuracy
0
Seconds Result
Transforming Ideas Into Impact
0
Input Parameters
0
Prediction Accuracy
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Maintenance Cost Reduction
Built on international standards for critical infrastructure
From CIGRE technical frameworks to EU AI Act requirements, RONIN AI is engineered to meet the regulatory demands of critical infrastructure operators while delivering actionable reliability insights.
CIGRE TB 761 · WG A2.49
Condition Assessment of Power Transformers
IThe international reference framework for transformer health index and fleet prioritisation. RONIN AI’s scoring methodology is built on this foundation.
ISO/IEC 42001:2023
AI Management System Standard
The world’s first certifiable standard for AI governance. RONIN AI’s development and operation align with its framework for risk management, transparency, and lifecycle accountability.
EU AI ACT READY
High-Risk AI as Critical Infrastructure
Regulation (EU) 2024/1689 classifies AI systems for critical infrastructure management as high-risk. RONIN AI is governed and documented accordingly – fully applicable from August 2026
From Substation to Strategy Room
RONIN AI transforms dissolved gas analysis, oil quality data, and operational history into fleet-wide reliability rankings, giving asset managers the intelligence to prioritize maintenance, extend transformer life, and prevent catastrophic failures.
How RONIN AI works
From CSV upload to reliability rankings. RONIN AI processes your transformer condition monitoring data through CIGRE-aligned algorithms and delivers actionable fleet intelligence in seconds.
Reliability intelligence in 30 seconds
Upload asset data. Get health index rankings. Prioritize maintenance.
Artificial Intelligence
We design the core infrastructure for the data-driven future in steel sector.
The Problem
Transformer fleets are managed on assumptions, not data.
Asset engineers and grid operators face the same structural challenge: too many transformers, too little time, and diagnostic data that sits in spreadsheets rather than driving decisions.
Ageing infrastructure, shrinking expert workforces, and increasing grid complexity have made reactive maintenance untenable. The industry needed a new standard.
Ageing infrastructure, shrinking expert workforces, and increasing grid complexity have made reactive maintenance untenable. The industry needed a new standard.
The Solution
RONIN AI ranks every asset in your fleet by actual risk.
Upload your condition monitoring data as a standard CSV file. RONIN AI processes it through a reliability-rank algorithm aligned with CIGRE TB 761 and returns a health index score for each transformer – with trend analysis, criticality classification, and recommended actions within 30 seconds.
Freelt Visibility
0
Monitor every transformer from 50 kVA to 800 MVA with unified health metrics. No asset left unranked, no critical failure undetected. One system, complete oversight.
Turning complex industrial problems into simple and powerful solutions
MASSIMILIANO VURRO
25+ years across regulatory systems, AI applications, and complex infrastructure. Three companies founded in fintech, eco-design, and AI. Patented systems for electrical network monitoring. Over five thousand certified products in machinery and electronics.
Clients include CERN, Siemens, Sky Group, China Mobile, Atlas Copco, Intertek, SGS, and FNA Group. Teaching at Politecnico di Torino. Contributor to the European AI Office’s work on EU AI Act implementation.
25+ years across regulatory systems, AI applications, and complex infrastructure. Three companies founded in fintech, eco-design, and AI. Patented systems for electrical network monitoring. Over five thousand certified products in machinery and electronics.
Clients include CERN, Siemens, Sky Group, China Mobile, Atlas Copco, Intertek, SGS, and FNA Group. Teaching at Politecnico di Torino. Contributor to the European AI Office’s work on EU AI Act implementation.
Six sectors. One shared problem.
01
Energy-Intensive Industries
Paper mills, steel and aluminium works, mining sites, nuclear research centres. Operations where unplanned transformer downtime directly stops production.
02
Grid Asset Owners
Stand-alone and grid asset owners, transmission and distribution utilities managing large transformer fleets with fixed maintenance budgets.
03
Transformer Manufacturers
OEMs integrating condition intelligence into post-sale service contracts and asset management proposals.
04
Testing Laboratories
Chemical and electrical testing facilities that generate diagnostic data and need to deliver actionable health assessments to their clients.
05
Service and Maintenance Companies
Field maintenance teams that need objective prioritisation data to allocate technician time and equipment across large transformer fleets.
06
Energy Sector Insurers
Specialist insurers in the energy sector requiring verified condition data to assess risk exposure on transformer assets.
No headquarters. No hierarchy. A shared technical standard.
Seetalabs network is a distributed group of power engineers, transformer testing specialists, utility asset managers, and field maintenance experts operating across Europe, Asia-Pacific, and the Americas.
The network connects domain expertise that no single organisation holds.
The common objective: making transformer reliability a data-driven discipline, not a matter of individual experience and institutional memory.
The network connects domain expertise that no single organisation holds.
The common objective: making transformer reliability a data-driven discipline, not a matter of individual experience and institutional memory.