Projects

Transform the higher-education experience through a scalable generative AI learning copilot that combines personalization, academic rigor and operational efficiency. IARA was created to address critical challenges in student engagement, content overload and personalized learning, while reducing repetitive faculty work. For students, the goal was to increase autonomy through multimodal learning support, competency-based assessment, personalized leveling recommendations and academic wellbeing monitoring. For faculty, it was to automate assessment support while preserving human academic validation. At institutional level, IARA aimed to establish a secure, scalable AI capability that reduces technology costs, strengthens educational innovation and creates reusable GenAI expertise across the Ânima ecosystem.

IARA transforms generative AI from a generic assistant into an academic learning copilot built around pedagogical governance. It combines validated knowledge bases, Ânima’s competency framework and mandatory faculty oversight with multimodal GenAI capabilities. Students receive personalized support across text, video and audio in three languages, competency-based assessments, leveling recommendations and academic wellbeing monitoring. Faculty gain AI support for competency-aligned question generation and open-answer correction with personalized feedback and grade suggestions, while retaining final academic validation. Designed for scalable use across the ecosystem, IARA brings personalization, assessment, wellbeing and faculty support into a single AI-driven learning experience.

IARA delivered measurable financial, experience and quality outcomes while establishing generative AI as a scalable institutional capability. The project eliminated BRL 1.5 million in SaaS contracts related to assessment support and avoided BRL 1 million in implementation costs for assessment solutions supporting the Competency Test and Academic Happiness Index. Veteran students participating in the 2025/1 pilot recorded NPS 6 points higher than non-participants. IARA also eliminated spelling errors and missing justifications in AI-generated exam questions. The project reached breakeven in six months, with estimated 2025 ROI above 100%. Beyond these measurable results, IARA is now used at scale across the Ânima ecosystem and has generated invitations to showcase the case at partner events, particularly national AWS events.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

Build RADAR as Union Agener's enterprise AI backbone for pharmaceutical manufacturing by turning daily operational discussions into accessible priorities and information aligned with the strategic plan. The deployed phase analyzes meeting transcripts, identifies critical and priority activities, retrieves information from earlier meetings and automatically distributes summaries across site TVs. It also connects 6,000 sensors for real-time performance against targets and lets department owners manage local TV content in the same system. Development ran from December 2025 to April 2026. The next planned layer comprises specialized Quality, Production and Engineering AI agents.

RADAR closes the gap between information shared in daily departmental meetings and the employees who need it. AI interprets transcripts, classifies the content, identifies critical and priority activities and retrieves earlier meeting information before automatically distributing summaries across site TVs. In the same system, department owners add information specific to their teams. For example, Quality can include release-related pending items listed in SAP to make the area's daily priorities clear. Combining common site information, historical context, departmental content and live performance against targets makes RADAR a foundation for a shared operating language and future AI capabilities. Specialized agents remain planned extensions.

Organizational climate surveys comparing 2025 and 2026 showed increases of 72 percentage points in site-wide communication, 43 percentage points in perceived employee engagement and 35 percentage points in trust in site leadership, as confirmed by the executive sponsor. RADAR supports access to operational information for an estimated 200 employees through the Daily Update and departmental TV displays. These survey outcomes accompany the rollout and broader site transformation; RADAR is not presented as their sole cause. The site forecasts a 45% production increase for full-year 2026. The sponsor identifies RADAR as an important enabler of alignment around that goal; this forecast is not a completed production result or an increase attributed solely to RADAR.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

Project CORTEX - Powering the Core of Textile Excellence is Artistic Milliners enterprise-wide digital transformation program, designed to create a connected, AI-enabled and data-driven digital backbone for a complex, vertically integrated textile and apparel business spanning Pakistan and the Western Hemisphere.

The program is transforming a decade old Oracle E-Business Suite (EBS) landscape into a unified, cloud based ecosystem for complex, multi-company and multi-location environment powered by Oracle Fusion Cloud ERP, SCM, HCM, Product Management, Plant Maintenance, Manufacturing, analytics, automation, and AI-driven capabilities.

CORTEX combines a large-scale extends beyond a conventional ERP transformation. It is focused on reimagining how the organization operates and makes decisions. Standardized processes, stronger governance, real-time information, integrated data and emerging AI capabilities are enabling greater visibility, faster decision-making, improved controls and operational agility across business divisions. The program is progressively introducing "AI agents and intelligent workflows" to enable faster decision-making, automate repetitive activities, identify exceptions, improve forecasting and provide business users with contextual insights directly within their operational processes.

Ultimately, Project CORTEX is building the digital nervous system of Artistic Milliners by connecting people, processes, data and intelligence across continents and establishing a scalable technology foundation for the company's next generation of textile excellence, sustainable growth and AI-enabled innovation.

From an operational perspective, integrated SCM, Manufacturing, Plant Maintenance, Inventory and Product Management capabilities provide improved visibility into materials, inventory, equipment and production processes. This creates the digital foundation for better resource utilization, optimized inventory levels, reduced process waste and more effective preventive maintenance.

CORTEX also strengthens the organization's ability to capture, govern and analyze sustainability-related data across companies, manufacturing locations and geographies. Integrated data, analytics and AI capabilities can increasingly support management in identifying inefficiencies, monitoring resource consumption and incorporating sustainability considerations into operational decision-making.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

Between January and December 2026 (first phase of a 2026-2028 roadmap), automate with Artificial Intelligence at least 12 recurring, low-value operating processes across Finance, Treasury, Accounts Payable, Sales/Marketing and Learning & Development, freeing the equivalent of 450+ person-hours per month currently spent on manual work - without growing the 7-person IT team or investing in physical infrastructure (0% CAPEX, 100% OPEX) - so operating capacity can scale with business growth, including expansion into two additional countries, without a proportional increase in organizational headcount.

What makes this project distinctive is its posture: a lean, seven-person IT function used AI not to add one more tool, but to redesign how a mid-sized, multi-country real estate developer operates. Rather than starting with technology, the program starts from one constraint - the organization must grow its business volume without growing headcount in the same proportion - and treats every recurring, low-value task as a candidate for an AI agent or automated workflow. It combines five otherwise separate disciplines (AI adoption, autonomous operations, data, cybersecurity/governance and commercial growth) into one governed portfolio with a single funding logic: fully cloud-based, zero capital investment, 100% operating expense. Equally notable is what it avoided: no "AI theater." Every initiative ties to a measurable operating indicator, and the riskiest part of the plan, per leadership, is adoption, not technology - so change management was funded as a core pillar, not an afterthought

As of September 2026, the first phase is already producing measurable results. Trust/loan disbursement packages for the bank now take under 5 minutes to assemble, down from roughly 30 minutes of fully manual work. The real-time inventory agent has recovered an estimated 840 hours per year of manual reporting capacity across all active developments. The commercial AI agent now delivers a first response to a new sales lead in under 5 minutes, compared to a process that previously depended entirely on staff availability. The new semantic data layer already supports roughly 90% of executive reports without an intermediate spreadsheet, up from about 20% before the project. The AI learning academy has been operating since June 2026, and cybersecurity awareness campaigns are sustaining a phishing-simulation pass rate above 95%. Purchase-order processing, launched in August 2026, is cutting per-document handling time from about 2 minutes to under 10 seconds. Beyond individual metrics.

— The project does not use solutions from the Global CIO catalog

— Profile in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

The goal of the project was to move TEMPO's fraud and cost control from manual, reactive reviews to a predictive, data-driven model. OCTO was designed to use Machine Learning to detect anomalies, cost distortions, and potential fraud across assistance service requests, and to direct analysts to the cases that matter most.

The project aimed to reduce financial losses, cut manual review effort, speed up investigations, and give the company the ability to scale its operations without losing control over costs and margins.

OCTO stands out by applying Machine Learning to a highly specific operational challenge: identifying anomalies, cost distortions, and potential fraud within assistance services. Rather than relying only on static rules or manual reviews, OCTO learns from historical patterns and highlights atypical behaviors that may indicate financial or operational risk.

Its uniqueness also comes from combining fraud prevention, cost intelligence, and operational efficiency in the same solution. OCTO does not replace human judgment; it makes it more effective by directing analysts to the cases that deserve attention most. This allows TEMPO to act earlier, scale controls, and protect margins with a more intelligent and data-driven approach.

OCTO delivered measurable financial and operational impact:

• Approximately BRL 3 million in annual cost avoidance, thanks to earlier and more accurate detection of anomalies, cost distortions, and potential fraud.
• Around 25% reduction in fraud-analysis headcount, as ML-based prioritization replaced broad manual reviews.
• Faster, more focused investigations: analysts now work on a prioritized list of suspicious cases instead of reviewing requests at random.
• Stronger cost control and scalability: the company can grow its volume of service requests while keeping controls consistent.

Overall, TEMPO shifted to a proactive approach to fraud prevention, with better decisions, protected margins, and a solid foundation for further use of AI in operations.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

The goal of the project was to build a modern, cloud-based digital foundation for Thal Limited that could support current business needs and future growth. The project aimed to migrate the core ERP environment to SAP S/4HANA RISE Cloud, improve system reliability, strengthen business continuity, and create a scalable platform for group-wide operations across 12 company codes. It also focused on introducing AI through SAP Joule, enabling faster access to business information, and improving user productivity. Another key goal was to provide leadership with real-time visibility through SAP Analytics Cloud dashboards and to simplify financial consolidation through SAP Group Reporting. Overall, the project was designed to reduce manual effort, improve decision-making, strengthen governance, and prepare the organization for future innovation.

This project is unique because it combined cloud ERP, artificial intelligence, executive dashboards, and automated group reporting into one connected transformation. Thal Limited did not only move SAP to the cloud; it also used the opportunity to modernize how the business works, reports, and makes decisions. The implementation covered 12 company codes, making it a group-wide initiative with strong business impact. SAP S/4HANA RISE Cloud created a scalable and reliable digital core, SAP Joule introduced AI-assisted user interaction, SAP Analytics Cloud provided real-time leadership dashboards, and SAP Group Reporting automated financial consolidation. This end-to-end approach made the project more than a technology upgrade—it became a business transformation platform for faster decisions, stronger governance, better visibility, and future innovation.

The project delivered a modern, cloud-based and AI-enabled SAP platform for Thal Limited. It improved system reliability, scalability, and business continuity by moving the core ERP environment to SAP S/4HANA RISE Cloud. SAP Joule introduced AI-assisted interaction, helping users get information faster and work more efficiently. SAP Analytics Cloud dashboards gave leadership real-time visibility of key business areas including finance, sales, supply chain, production, workforce, and health and safety. SAP Group Reporting improved the financial close process by automating consolidation, reducing manual effort, eliminating inter-company transactions, and increasing reporting accuracy. Overall, the project enabled faster decision-making, stronger governance, improved operational efficiency, and created a future-ready digital foundation for continued innovation across the organization.

— The project does not use solutions from the Global CIO catalog

— Profile in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

Replace manual logbooks and shift reports with automatic, real-time measurement of the company's milling and packing operations. Objectives:

• calculate OEE (availability, performance, quality) automatically every shift.

• track yield and extraction against every active production Job Order.

• inspect every moving bag on the packing lines inline by computer vision and reject defective bags without stopping the line.

• alert the responsible teams in real time to prolonged stoppages, abnormal extraction rates and quality failures.

• link operational data to the corporate ERP environment so production performance is measured, attributed and reported without human transcription.

Most milling operations still reconstruct performance after the fact from paper logbooks. This project replaced that with automatic measurement at production speed. Its most distinctive element is inline quality inspection by computer vision, implemented and running in production since the first Q2 2026: a live stream is analysed continuously for packaging anomalies, printing errors, missing or empty date codes and structural defects on moving bags; a defect triggers an inline rejection that isolates the bag mechanically while the reason is logged automatically. That is inspection of every bag at line speed, not sampling after the fact. Around it, an automatic OEE engine and order-based yield tracking make every loss visible by cause and every kilogram accountable against its Job Order, on one live screen across the mills, the feed mill and the packing lines.

Measured on the live platform (81.56% availability, 77.91% performance, 100% quality), zero scrap with 880 units diverted to rework equal to 0.16% of output, 98.92% yield with a 75.28% extraction rate and milling loss of 1.08% against the 1.5% usual in commercial milling, and computer vision inspection live on five packing lines rejecting defective bags inline at production speed. Every loss is now attributed by cause; including 130 unclassified stops, had never been visible before. Nine workstations are monitored on one live screen, replacing manual logbooks, and the health and safety extension of the same platform is implemented and running. The OEE figure is the operation's first continuously measured baseline, so no before and after comparison is claimed.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

Transform fragmented eSocial operations across São Paulo State into a unified, auditable digital governance layer supporting 1.1 million active, retired and pensioner records, 2,500 legal entities and 47 public bodies. The award scope operationalizes Portaria RFB 632/2025 and the governance model required by Decree 70.693/2026. Objectives include standardized ETL and validation across more than 40 source systems, transmission/reception with protocol- and receipt-level evidence, a historical XML mirror since November 2021, an authoritative Single Source of Truth, continuous reconciliation, automated mass remediation, fiscal/CND risk control and executive auditability. By the award cut-off, all required eSocial reporting from November 2021 through the current reporting month will be remediated, transmitted and accepted by the federal environment with traceable protocol and receipt evidence.

The project operates at unusual public-sector scale: 1.1 million people, 2,500 legal entities, 47 public bodies, more than 40 heterogeneous source systems and more than 116 million historical XML records. A conventional eSocial messaging solution transports events. This program reconstructs, validates and governs the complete compliance lifecycle: source data, transformation rules, transmission, federal acceptance/rejection, receipts, historical reconciliation and mass remediation. It establishes an auditable Single Source of Truth and supports automatic, large-scale rectification, exclusion, reopening, closing, retransmission and historical reprocessing. More than 1 million inconsistencies have already been remediated; about 4 million events have been transmitted; and assisted remediation has raised acceptance from roughly 72% at initial processing to above 98%.

More than 1 million inconsistencies have already been remediated and approximately 4 million eSocial events transmitted, with approximately 11 million expected by 31 October 2026. During September 2026, the approximately R$2.2 billion public fiscal divergence is being reduced at about 15% per week, with near-zero targeted by month-end; the final achieved result must be refreshed before submission. CND impacts are actively identified and treated monthly, and the platform automatically monitors data conditions related to special-retirement rights. Phase 1 concludes when all required reporting from November 2021 through the current month has been accepted by the federal environment with protocol and receipt evidence.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

1. To design and implement an AI-powered solution that integrates with OTDR testing systems.

2. The system will automatically identify and classify fiber, tube, and cable colors across diverse Optical Fiber Cable (OFC) types, improving accuracy, speed, and reliability in OTDR testing of various stages of operations.

·       AI-powered color recognition model.

·       Integrated OTDR + AI software module.

·       Technician dashboard with visualization and reporting features.

·       Training dataset and documentation.

·       Pilot deployment and field validation results.

The AI-based OTDR testing system is installed and running at our Hyderabad plant on a dedicated NVIDIA Jetson AI unit. It was commissioned and tested on site on 10–11 August 2026. Multi-Tube and IBR cables were tested end to end; Flat Ribbon testing is to be completed by our QA team.

1. Ribbon ID exact match 96.5%.

2. 96.2% fiber color matching.

3. 95.4% tube color matching.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog

The goal was to transform enterprise IT support from a traditional ticket-driven model into an AI-first, conversational self-service experience. IT Sensei was designed to give 6,500 employees instant access to IT knowledge and assistance through natural language, reduce repetitive L1 support effort, improve response time and employee experience, and increase the utilization of enterprise knowledge.

A parallel objective was to establish a secure, scalable and governed Generative AI capability that could integrate with existing IT services rather than operate as a standalone chatbot. The project targeted measurable production adoption while retaining identity controls, security governance, monitoring and human escalation for complex cases.

IT Sensei is not a conventional FAQ bot or GenAI proof of concept. It creates a conversational operating layer between employees, enterprise knowledge and IT services.

Its core principle is “AI first, human when required”: Generative AI handles appropriate routine interactions while complex, sensitive or exceptional cases remain within controlled human support processes.

The solution combines Generative AI, enterprise knowledge, IT workflows, identity and access controls, security governance and operational monitoring into one employee-facing experience. It demonstrates GenAI operating at production scale rather than remaining a pilot: approximately 6,500 users and 50,000 interactions per month, with around 30% of interactions AI-resolved or assisted.

The architecture also establishes a reusable enterprise pattern for extending AI-assisted operations beyond IT support.

IT Sensei achieved measurable production adoption and operational impact:

• 6,500 employees/users covered by the solution. 

• Approximately 50,000 interactions/month, equivalent to about 600,000 interactions annually at the current run rate.

• Approximately 30% of interactions AI-resolved or AI-assisted. 

• Approximately 8 minutes average L1 effort per query, providing a measurable baseline for support effort addressed by the platform. 

• Faster employee access to IT knowledge through natural-language interaction. 

• Reduced dependency on human intervention for appropriate routine queries. 

• Greater ability for L1 teams to focus on complex incidents and exceptions. 

• Established a reusable, governed enterprise architecture for integrating GenAI with knowledge and IT workflows.

The project therefore delivered both a measurable operational outcome and a scalable foundation for broader enterprise AI adoption.

— The project does not use solutions from the Global CIO catalog

— The project manager did not take part in the ranking «Top 100 IT Leaders»

— The supplier profile is not present in the Global CIO catalog