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We are proud to share an important milestone in Aignosi’s international journey. &nbsp;Aignosi has officially become a member of the Global Community on Artificial Intelligence for Industry and Manufacturing, an initiative led by <a data-mce-href="https://www.unido.org/aim-global-betaversion" href="https://www.unido.org/aim-global-betaversion" target="_blank" rel="noopener">UNIDO – the United Nations Industrial Development Organization</a>.This global community was created to accelerate the responsible, sustainable, and scalable adoption of Artificial Intelligence in industrial and manufacturing environments. It brings together manufacturers, industrial AI solution providers, academic institutions, governments, regulators, and key players from the global economy with a shared objective: turning AI into real, measurable industrial impact.<h3>Why UNIDO’s Global AI Community Matters </h3>Industrial AI is no longer an experimental technology. It is becoming a strategic pillar for productivity, resilience, sustainability, and competitiveness across global value chains. UNIDO’s initiative plays a critical role in:<ul><li>Connecting industrial and manufacturing ecosystems across regions and continents</li><li>Promoting best practices for trustworthy, ethical, and explainable AI</li><li>Supporting technology transfer and capacity building</li><li>Aligning industrial innovation with the UN Sustainable Development Goals (SDGs)</li></ul>By joining this community, Aignosi become part of a global effort to shape how AI is designed, deployed, and governed in real-world industrial operations.<h3>What This Means for Aignosi </h3>Becoming a member of UNIDO’s Global Community on AI for Industry and Manufacturing represents more than recognition. It is a strategic step that reinforces our long-term vision for AI-driven industrial transformation.Strengthening Our International Presence Participation in this global initiative expands Agnosi’s international footprint, opening doors to collaboration with industrial leaders, technology innovators, and institutions worldwide. It positions Aignosi within a trusted global network where industrial AI strategies are discussed, challenged, and refined.Enabling Global Collaboration and Innovation The community fosters technical cooperation across continents, enabling joint innovation initiatives, research exchange, and the development of scalable industrial AI frameworks. For Aignosi, this means accelerating learning and co-creation in areas such as:<ul><li>Predictive and prescriptive maintenance</li><li>AI at the edge for OT environments</li><li>Intelligent process optimization</li><li>Industrial sustainability and energy efficiency</li><li>Secure and interoperable IT/OT architectures </li></ul>Advancing Knowledge Sharing and Best Practices Industrial AI success depends on more than algorithms. It requires governance, data quality, MLOps, DataOps, and deep integration with operational technology. Through this community, we actively contribute to — and learn from — global best practices that help bridge the gap between experimentation and production-scale AI in manufacturing.Contributing to Ethical and Sustainable Industrial Development At Aignosi, we strongly believe that industrial AI must be ethical, inclusive, and outcome-driven. Our participation supports the development of AI solutions aligned with sustainability, workforce empowerment, and long-term industrial resilience — fully connected to the UN SDGs.<h3>Reinforcing Our Commitment to Intelligent Industrial Transformation </h3>This milestone reinforces our commitment to delivering AI and Machine Learning solutions that connect Operational Technology (OT) with measurable business impact. It also strengthens our role as a thought leader in Industrial AI, Industry 4.0 and the emerging Industry 5.0 paradigm, where technology and human expertise evolve together.By engaging in UNIDO’s global AI ecosystem, Agnosi and the IAG Group are not only following global trends — we are actively helping shape them.We remain focused on what matters most: transforming industrial data into intelligence, intelligence into decisions, and decisions into sustainable competitive advantage — now with an even stronger voice on the international stage.&nbsp; About usAignosi, specializes in applying Artificial Intelligence and Machine Learning to industrial and operational environments, bridging IT and OT to deliver real-world impact in manufacturing, energy, and critical infrastructure.

Aignosi Joins UNIDO’s Global Community on AI for Industry and Manufacturing

<h3>TL;DR</h3>&nbsp;🔍 Advancing Operational Reliability: Aignosi and Alcoa Launch High-Frequency AI Fault Detection Project at Alumar &nbsp;In global aluminum production, operational reliability is everything. The electrolytic reduction process—the core of aluminum smelting—is a highly complex, energy-intensive, and sensitive operation. Small anomalies can rapidly escalate into costly disruptions, impacting safety, efficiency, equipment life, and environmental compliance.That is why Alcoa, one of the world’s largest aluminum producers, has initiated a strategic innovation project at Alumar, its major industrial operation in São Luís, Maranhão, Brazil. And Aignosi is proud to be the chosen partner for this challenge.Together, we are developing an AI-driven high-frequency fault detection model, designed to identify early-stage anomalies in reduction cells by analyzing high-resolution electrical signals—an unprecedented step toward predictive intelligence in heavy industry.&nbsp;<h3>🏭 About Alcoa and Alumar: A Global Powerhouse in Aluminum Production &nbsp;</h3>Alcoa has long been recognized as an industry leader in sustainable aluminum production, advanced metallurgical processes, and digital transformation.Alumar, one of Alcoa’s key operations in Brazil, plays a central role in the company’s global value chain.In a facility of this scale, operational performance hinges on the stability of the electrolytic reduction curves, where aluminum is produced through continuous high-current electrolysis. The process is sensitive, interdependent, and extremely demanding from an electrical and thermodynamic standpoint.Which brings us to the challenge.<h3>⚙️ The Challenge: Detecting Critical Failures in Electrolytic Reduction Cells &nbsp;</h3>Reduction cells operate under massive electrical loads, involving complex variables such as: <ul><li>high DC current</li><li>cell voltage</li><li>magnetic and thermal interactions</li><li>bath chemistry</li><li>dynamic resistance</li><li>high-frequency waveform distortions &nbsp;</li></ul>A small disturbance—often starting as an imperceptible fluctuation—can indicate: <ul><li>loss of thermal stability</li><li>short circuits</li><li>abnormal anode behavior</li><li>early cell deterioration</li><li>inefficiencies in energy conversion</li><li>material buildup or crusting issues &nbsp;</li></ul>Traditional monitoring tools capture low-frequency trends well—but they often miss micro-anomalies hidden in the high-frequency electrical spectrum.These subtle signatures can carry the first signs of critical failures.<h3>🔬 The Innovation: High-Frequency AI Models for Current &amp; Voltage Waveforms &nbsp;</h3>Aignosi was selected to develop a next-generation fault detection model capable of analyzing:<ul><li>high-frequency current signals</li><li>high-frequency voltage signals</li><li>harmonic distortions</li><li>fast transient behaviors</li><li>electrical noise patterns linked to failure modes &nbsp;</li></ul>This approach unlocks a new predictive layer for the reduction process. Our methodology leverages:✔ Advanced frequency-domain signal processing &nbsp;Extraction of meaningful features from spectral components, harmonics, and transient patterns.✔ AI/ML models trained specifically for OT environments &nbsp;Architectures optimized for high-variability, high-noise industrial data.✔ Edge Intelligence &nbsp;Capability to deploy models on edge devices for real-time detection with minimal latency.&nbsp;✔ AIOps and MLOps pipelines &nbsp;Ensuring reliable deployment, monitoring, governance, and lifecycle management of models in production.The outcome is a model capable of identifying incipient failure signatures, hours—or even days—before traditional alarms would trigger.<h3>🚀 Why This Project Matters for the Aluminum Industry &nbsp;</h3>Electrolytic reduction is the backbone of aluminum manufacturing. Bringing high-frequency AI intelligence into this environment unlocks significant benefits:🔹 Higher operational reliability &nbsp;Early detection prevents disruptions in reduction curves.🔹 Improved energy efficiency &nbsp;Reducing instability directly lowers energy waste—critical in one of the world’s most energy-intensive processes.🔹 Extended equipment and cell lifespan &nbsp;Lower thermal and electrical stress increases asset longevity.🔹 Enhanced safety &nbsp;Predicting hazardous conditions reduces operational risk for workers and equipment.🔹 A new standard for industrial digitalization &nbsp;Few industries have ventured into high-frequency AI modeling in OT environments—this collaboration sets a benchmark.For Alcoa, it reinforces global leadership in technology-driven performance.For Aignosi, it highlights our core mission: bringing intelligent reliability to the industrial world.<h3>🤝 Co-Creating the Future: Aignosi + Alcoa &nbsp;</h3>This project is built on true co-creation.Our teams—data scientists, process engineers, OT specialists, and field operators—are working side by side to align:<ul><li>domain expertise</li><li>operational constraints</li><li>advanced algorithms</li><li>real-time integration needs &nbsp;</li></ul>The result is a solution engineered not only for accuracy, but also for industrial robustness, interoperability, and long-term scalability.And most importantly:<blockquote>it is a step toward an industrial ecosystem where critical processes become increasingly autonomous, resilient, and intelligent.</blockquote><h3>⚙️ Conclusion: High-Frequency AI Is Opening a New Era of Industrial Predictive Intelligence &nbsp;</h3>The collaboration between Alcoa and Aignosi at Alumar goes beyond a technological deployment—it represents a new chapter in how AI can enhance heavy industry operations.By capturing high-frequency electrical signals and transforming them into predictive insights, we are unlocking a new dimension of operational intelligence for the aluminum sector.The future of industrial reliability is:data-driven, edge-enabled, and powered by AI that understands the complexity of OT environments.And together, we are building that future in Maranhão—one reduction cell at a time.

High-Frequency AI Fault Detection Project at Alumar

The industrial sector is living through a pivotal moment: the convergence of AI, IoT, and Operational Technology (OT) is reshaping how factories operate, optimize, and evolve. Within this context, we are proud to share a milestone that validates our commitment to driving intelligent, data-driven transformation across industrial environments.Aignosi has been named one of Brazil’s Top 10 IoT startups in the 2025 edition of the 100 Open Startups Ranking— one of the country’s and global most respected benchmarks for open innovation, technological impact, and collaboration between industry leaders and disruptive technology companies.This recognition goes far beyond an award.<blockquote>It is a data-driven confirmation that our strategy — combining Industrial AI, IoT, AIOps, MLOps, and Edge Intelligence — is generating measurable value for the industrial ecosystem.</blockquote><h3>Strengthening Reliability and Intelligence in Industrial Operations &nbsp;</h3>In industries where uptime, safety, and process efficiency are non-negotiable, IoT has become the backbone of digital modernization. Being ranked among the top companies in this space reinforces our mission:<blockquote>to elevate operational intelligence through robust, interoperable, and AI-driven industrial solutions.</blockquote>Every integration performed on the plant floor, every predictive model deployed at the edge, every advance in DataOps, and every automation built on top of complex OT environments contributes to this mission. We see this achievement as a collective result — one powered by engineering rigor, scalable architectures, and a deep understanding of the realities of industrial operations.<h3>Why This Recognition Matters for the Future of Industrial Digitalization &nbsp;</h3>The 100 Open Startups ranking reflects a broader movement: the industry is accelerating toward a connected, intelligent, and resilient operational model.Our work at the intersection of AI and OT aims to support this shift through:<ul><li>Predictive and prescriptive intelligence at the edge, reducing latency and improving decision-making where it matters most</li><li>AIOps frameworks that bring autonomy, observability, and scalability to mission-critical industrial systems</li><li>MLOps and DataOps pipelines designed for high-frequency, high-variability industrial data</li><li>Interoperable architectures that bridge IT and OT environments without compromising performance or security </li></ul><span style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);" data-mce-style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);">Recognition as a leader in IoT strengthens our role in guiding this transformation — not only with technology, but with thought leadership and practical impact.<h3>A Shared Achievement &nbsp;</h3>We extend our appreciation to our partners, customers, and the broader community that believes in the power of Industrial AI. Every project, every co-creation initiative, and every deployment reinforces our belief that:<blockquote> the future of industry is connected, intelligent, and continuously learning.</blockquote>And we are committed to building this future — side by side with those shaping the next generation of industrial performance.⚙️🤝💙The Aignosi Team

Recognized Among Brazil’s Top 10 IoT Startups in the 2025 100 Open Startups Ranking

<h3>TL;DR</h3>In the mining sector, achieving operational gains often means years of mechanical upgrades, process fine-tuning, and heavy capital investment. But when artificial intelligence, reliability engineering, and operational technology (OT) work in harmony, improvements become exponential — and immediate. This is exactly what happened at the BSM-3 crushing plant at Vulcan International coal plant in Moçambique, which delivered record-breaking results through an Industrial AIOps approach focused on optimization, autonomous process control, and systemic reliability.&nbsp; &nbsp;<h3>Record Results That Redefine Operational Performance &nbsp;</h3>Within hours of deploying the industrial AI platform, BSM-3 achieved milestone performance indicators:<ul><li>4,400 t/h feed rate — new operational record. </li><li>82,000 tons/day — highest daily throughput ever recorded, a 32% increase over the previous peak.</li><li>100% of critical events eliminated — no feeder failures, blockages, or overload trips since adoption.</li><li>+12 percentage points in BIN-03 level stability — critical for maintaining continuous flow. &nbsp;</li></ul>These aren’t just efficiency gains — they represent true reliability transformation, the cornerstone of high-performance OT environments.<h3>From Reactive Maintenance to Proactive, AI-Driven Operations &nbsp;</h3>According to Vulcan's report, BSM-3 underwent a full performance transformation by combining advanced analytics, predictive models, automated setpoint adjustments, and control-system improvements.&nbsp;🔧 Mechanical &amp; Process Enhancements &nbsp;<ul><li>Significant reduction in unplanned downtime.</li><li>Consistent and stable ramp-up execution.</li><li>Removal of classic bottlenecks such as blockages and overload trips. &nbsp;<span style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);" data-mce-style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);">&nbsp;</li></ul>🤖 AIOps for Reliability &nbsp;<ul><li>Real-time failure prediction before deviations become critical.</li><li>Dynamic process adjustments based on actual conditions.</li><li>Continuous insights into equipment and system behavior. &nbsp;<span style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);" data-mce-style="font-family: inherit; font-size: calc(18px * var(--body-fontSize, 1)); font-style: var(--body-fontStyle, normal);">&nbsp;</li></ul>📉 Direct Impact on Cost per Ton &nbsp;<blockquote>A 25% increase in throughput directly translated into lower unit costs — one of the most strategically relevant KPIs for industrial operations.</blockquote><h3>Why This Project Matters for the Entire Industry &nbsp;</h3>What’s happening at BSM-3 is not an isolated optimization case — it’s a clear indicator of the maturity of AI within OT environments. Unlike traditional improvement programs that require long ramp-up periods, the results achieved were:<ul><li>Immediate — benefits observable from day one.</li><li>Sustainable — maintained through continuous AI learning.</li><li>Scalable — enabling the plant to approach future targets of over 100,000 tons/day. &nbsp;</li></ul>This level of transformation is only possible when AI, MLOps, DataOps, and reliability engineering operate cohesively within the OT landscape.<h3>A Glimpse of Industry 4.0 and 5.0 in Action &nbsp;</h3>The BSM-3 success story reinforces an irreversible trend:➡️ The next wave of industrial productivity will come from intelligence, not infrastructure.With dedicated Industrial AIOps platform, in this case, SIENTIA™, plants can:<ul><li>Operate with predictive precision,</li><li>Anticipate anomalies,</li><li>Increase system resilience, Unlock previously hidden performance potential. &nbsp;</li></ul>This is not simply automation. It is operational augmentation — enabling plants to self-optimize while enhancing human decision-making.<h3>Conclusion: The New Paradigm of High-Performance Industrial Operations &nbsp;</h3>The evolution of the BSM-3 crushing plant demonstrates that integrating AI, OT engineering, and industrial process expertise can define a new operational baseline — not incremental, but transformational.By eliminating critical events, boosting stability, increasing throughput, and reducing costs simultaneously, BSM-3 proves that AI-driven reliability is now a direct competitive advantage.And this transformation continues to accelerate as models learn, adapt, and reinforce a culture of data-driven, high-reliability operations.

25% More Throughput in Under 24 Hours: Vulcan’s AI-Powered Breakthrough

<h3>TL;DR</h3>We are proud to announce a new step in our journey with <a data-mce-href="https://www.votorantimcimentos.com.br" href="https://www.votorantimcimentos.com.br" target="_blank" rel="noopener">Votorantim Cimentos</a>, one of the largest cement producers in the world. This project is a milestone in applying Industrial AI and real-time inferencing in Operational Technology (OT) to unlock efficiency and sustainability in clinker kiln operations.<h3>From Laboratory Delays to Real-Time Insights </h3>In cement production, the clinker kiln is the most critical and energy-intensive asset. Advanced Process Control (APC), typically based on Model Predictive Control (MPC), depends heavily on timely and reliable data to keep the kiln stable, efficient, and compliant.Until now, many of these critical variables — such as CO, NOx, and O₂ concentrations in the kiln flue gas — have relied on laboratory measurements or on probes subject to premature wear and downtime. That meant waiting up to two hours for key measurements, limiting the responsiveness of controllers and exposing the operation to inefficiencies and risks.<h3>Enter SIENTIA™: AI at the Edge of OT </h3>With Aignosi’s SIENTIA™ Industrial AIOps platform, Votorantim Cimentos can now deploy virtual sensors that estimate these critical variables in real time, directly within the OT environment.This unlocks two immediate benefits:🔹 Real-time inferencing for APC/MPC — controllers no longer need to wait for delayed lab data. Instead, they can use AI-powered estimates every second to stabilize the kiln, optimize fuel efficiency, and reduce emissions.🔹 Reduced probe wear and maintenance costs — by relying less on physical probes exposed to extreme conditions in the kiln smoke box, the plant extends equipment life and avoids unplanned downtime.<h3>Scaling AI in Cement Production </h3>The true value of SIENTIA™ lies not only in enabling these models but also in ensuring they remain reliable, observable, and governable at scale.<ul><li>Models run fully on-premise, respecting the strict latency and cybersecurity requirements of OT.</li><li>Automated retraining and end-to-end observability ensure long-term accuracy and trustworthiness.</li><li>Integration with APC systems creates a closed feedback loop where AI and control engineering work hand in hand. </li></ul><blockquote>This is not just about deploying one model. It’s about building a scalable foundation for dozens of AI models in cement production, creating a new paradigm for operational excellence and sustainability.</blockquote><h3>A Shared Vision for the Future </h3>Together with Votorantim Cimentos, we are setting a new benchmark for the cement industry: real-time AI seamlessly embedded in kiln operations, empowering engineers and controllers with insights that were previously out of reach.The clinker kiln may be centuries old in concept, but with Industrial AI, it is entering a new era of efficiency, resilience, and intelligence.

Unlocking Real-Time AI in Cement Production: Aignosi and Votorantim Cimentos Advance Clinker Kiln Optimization

<figure id="figure-5m2thean6mftsqatm"><img id="5m2thean6mftsqatm" src="https://cdn.durable.co/blocks/21jsej0gTgeQ6plkuIlEYUR5LXoSrKVHBf5jlgyHHs8t44SB0ABK8SVZi6XHYNzo.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/21jsej0gTgeQ6plkuIlEYUR5LXoSrKVHBf5jlgyHHs8t44SB0ABK8SVZi6XHYNzo.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>TL;DR;</h3>For the second consecutive year, Aignosi had the privilege of participating in<a href="https://www.abmbrasil.com.br/evento/abm-week-9" target="_blank" rel="noopener" data-mce-href="https://www.abmbrasil.com.br/evento/abm-week-9"> ABM Week 2025</a>&nbsp;the largest technical event for the steel and mining value chain in Brazil.This edition was particularly meaningful: together with our partner <a data-mce-href="https://www.samarco.com" href="https://www.samarco.com" target="_blank" rel="noopener" data-mce-selected="inline-boundary">Samarco</a>, one of the biggest mining companies in Brazil, we presented a peer-reviewed technical paper at the conference — a milestone that highlights how Artificial Intelligence is creating measurable impact in complex operational environments.<h3>From Concept to Production: Virtual Sensors in Mining Operations </h3>The paper, “Scaling AI and Machine Learning in Samarco’s OT”, describes how two virtual sensors, developed and deployed in Samarco’s iron ore operations, are driving operational efficiency:Real-time mass flow estimation on conveyor belts, eliminating the need for additional instrumentation. Silica content prediction in rougher flotation, directly feeding advanced controllers to optimize recovery. <blockquote>These models are not experimental. They are in production today, automatically retrained, with end-to-end observability and running fully on-premise to meet OT cybersecurity and latency requirements.</blockquote><h3>Scaling AI in OT: A New Benchmark </h3>The deployment of these virtual sensors was made possible through our SIENTIA™ Industrial AIOps platform, which enabled:<ul><li>15 models deployed in less than 3 months</li><li>A governance framework ensuring security and reliability</li><li>A scalable foundation for future AI use cases</li></ul>Looking ahead, Samarco plans to scale to around 50 models by the end of 2025, consolidating one of the largest industrial AI initiatives in OT environments in Brazil.This demonstrates that scaling AI in heavy industry is not just aspirational — it is achievable, when rooted in operational reality.<h3>Key Insight: Scaling Starts with What Works </h3><blockquote>A critical lesson from this journey: scaling AI in industry doesn’t start with deep learning. It starts with virtual sensors that work — and work well.</blockquote>When organizations deploy 15 models in less than 90 days — and project 50 by year’s end — it signals more than technological advancement. It shows real impact in industrial operations: fewer unplanned stoppages, higher process stability, and improved productivity.<h3>Gratitude and Collaboration </h3>We thank Samarco for leading this work, especially Jeancarlo, whose vision and leadership were instrumental throughout this transformation.It was also an honor to share this achievement with professionals and companies shaping the future of the industry, including colleagues from Vale, Stefanini IHM, Usiminas, and all who contributed to the technical discussions at ABM Week.<h3>&nbsp;Conclusion </h3>Industrial AI is no longer a distant promise. As Samarco’s journey shows, it is scalable, sustainable, and operationally impactful — even in environments with the strict constraints of OT.The path forward is ambitious, but the foundation is clear: governance, scalability, and trust in AI systems that deliver measurable value.

Blog

Aignosi Joins UNIDO’s Global Community on AI for Industry...

High-Frequency AI Fault Detection Project at Alumar

Recognized Among Brazil’s Top 10 IoT Startups in the 2025...

25% More Throughput in Under 24 Hours: Vulcan’s AI-Powere...

Unlocking Real-Time AI in Cement Production: Aignosi and ...

Scaling Industrial AI in Samarco's OT: Lessons from Samar...