Xelpmoc Design and Tech Ltd Management Discussions

Management Discussion and Analysis

Global Economy

Global growth is projected to slow to 3.1% in CY2026 and 3.2% in CY2027 under the IMFs April 2026 reference forecast, a downward revision from the steadier 3.3% path projected as recently as January 2026, following the outbreak of conflict in the Middle East, which has disrupted commodity markets, lifted energy prices and dented business confidence. Even setting aside the conflicts impact, global growth remains below the historical average of 3.7% recorded from CY2000 to CY2019. Headline inflation is expected to tick up to around 4.4% in CY2026 on firmer energy and food prices before resuming its decline in CY2027, with the pressure concentrated in emerging market and developing economies, particularly commodity-importing countries with limited fiscal buffers.

Among major economies, Chinas growth is projected to moderate to 4.4% in CY2026 (from around 5% in CY2025), a milder slowdown than earlier feared after a reduction in effective US tariff rates and additional domestic fiscal support. The United States is forecast to expand by around 2.4% in CY2026, aided by fiscal policy, a lower policy rate and tax incentives for corporate investment under recent legislation, even as the drag from higher trade barriers gradually fades. Advanced economies as a group are projected to grow 1.8% in CY2026, while several major economies (including Germany, whose manufacturing and export sectors remain under pressure, and Japan, still working through the effects of prior supply disruptions) continue to lag the broader recovery.

Core goods disinflation has broadly run its course, but services price inflation remains above pre-pandemic averages in several advanced economies, keeping central banks cautious and contributing to continued divergence in monetary policy across countries. The IMFs Spring 2026 assessment flags clear downside risks: a longer or broader Middle East conflict, deeper geopolitical fragmentation, a reassessment of Al-driven productivity expectations, or renewed trade tensions could all weaken growth further and unsettle financial markets, while elevated public debt and eroding institutional credibility continue to narrow the room for policy manoeuvre in many economies.

Source: IMF

Indian Economy

India remained the fastest-growing major economy through FY2025-26. The Reserve Bank of India revised its full-year real GDP growth projection upward through the course of the year (from an initial 6.5% to 6.8%, and most recently to 7.3%) as broad-based momentum built across sectors. Real GDP grew 7.8% in Q1 FY2025-26 and accelerated further to 8.2% in Q2, the fastest pace in several guarters, led by resilient private consumption, front-loaded government capital expenditure, and easing financial conditions. International agencies have echoed this optimism: the World Bank projects growth of around 6.5% for 2026, the IMF has raised its estimate to 6.2%.

Inflation trends were unusually benign during the year. Headline CPI inflation, which stood near 4.3% at the start of CY2025, softened steadily through the year to reach historic lows of around 0.25% in October 2025 (the lowest level in the current CPI series) before edging up modestly by year-end. The decline was driven by a prolonged, nine-month fall in food prices and the disinflationary effect of GST rate rationalisation implemented from September 2025, which directly touched roughly 11.4% of the CPI basket. Wholesale price inflation turned negative over the same period, reinforcing the broader picture of easing input costs. Indias external position also strengthened: the current account deficit narrowed to around 1.3% of GDP in Q2 FY2025-26 on the back of robust services exports and a 10.7% year-on-year rise in remittances, while gross FDI grew 19.4% and net FDI more than doubled in April - September FY2025-26 compared with the prior year.

Government continued efforts to enhance Indias capabilities and global standing in Al this year, deepening investment under the IndiaAl Mission and expanding centres of excellence focused on Al applications in healthcare, agriculture and sustainable urban development, while the RBI advanced initiatives to build FinTech and AI/ML data repositories to support informed policymaking around technology adoption in the financial sector.

Looking ahead to FY2026-27, growth is expected to moderate slightly but remain robust, with the RBI projecting around 6.6% for the year, supported by sustained government capital expenditure, and strengthening consumer and business confidence. Global trade uncertainty, geopolitical tensions (including the conflict in the Middle East and its bearing on energy prices) and financial market volatility remain the principal risks to this outlook, even as Indias structural growth drivers, including continued reform momentum and its expanding digital and Al ecosystem, remain intact.

Source: PIB, Gol, IMF

Global IT Spend

Worldwide IT spending is projected to reach US$ 6.31 trillion in CY2026, an increase of 13.5% over CY2025, according to Gartner, Inc.s April 2026 forecast, a further upward revision from Gartners earlier CY2026 estimates of US$ 6.08 trillion (October 2025) and US$ 6.15 trillion (February 2026). Each successive revision has reflected accelerating investment in AI infrastructure and advanced memory, underscoring how quickly enterprise and hyperscaler AI spending intentions have continued to firm through the year.

Data center systems spending is set to register the sharpest growth of any category in CY2026, rising 55.8% to surpass US$ 788 billion, as hyperscale cloud providers continue to invest heavily in servers and high-performance compute optimised for AI workloads, with robust demand for high- bandwidth memory adding further pricing pressure across the semiconductor supply chain. IT services (spanning application and infrastructure implementation, managed services and infrastructure-as-a-service) remains the single largest spending category, expected to exceed US$ 1.87 trillion in CY2026. Software spending is projected to grow 15.1% to reach US$ 1.44 trillion, with generative AI (GenAI) model spending growth expected to more than double year-over-year, as GenAI features become a standard, embedded component of enterprise software rather than a differentiator. Communication services spending is forecast at US$ 1.36 trillion (up 4.8%), while device spending is expected to reach US$ 856 billion (up 8.2%), with growth tempered by rising memory costs that are pushing up average selling prices and constraining replacement cycles at the lower end of the market.

Segment	CY2025	CY2025	CY2026	CY2026
	Spending	Growth	Spending	Growth
	($M)	(%)	Estimated ($M)	Forecast (%)
Data Center Systems	506,000	31.7	788,000	55.8
Devices	791,000	9.1	856,000	8.2
Software	1,251,000	11.5	1,440,000	15.1
IT Services	1,648,000	3.8	1,870,000	13.5
Communication Services	1,298,000	1.2	1,360,000	4.8
Overall IT	5,560,000	8.7	6,310,000	13.5

Indian IT Spend

Indias IT spending is projected to reach US$ 176.3 billion in CY2026, an increase of 10.6% over CY2025s roughly US$ 160 billion, according to Gartner. This growth continues to outpace the global average, driven by accelerating enterprise adoption of cloud, AI and digital technologies. The data centre systems segment is projected to record the highest growth of any category in India, expanding 20.5% in CY2026 (moderating from 29.2% in CY2025), fuelled by substantial AI infrastructure investment, the entry and expansion of large domestic players such as Reliance and the Adani Group into the data-centre business, and evolving data-localisation and sovereign- cloud requirements.

Software spending in India is forecast to grow 17.6% to US$ 24.7 billion in CY2026 as enterprises ramp up investment in AI-enabled applications and modern IT infrastructure, while IT services spending is projected to grow 11.1%, with sustained double-digit growth of 12-14% expected over the following years, driven by continued expansion of Global Capability Centres (GCCs) and Indias cost-competitive, skilled technology workforce. End-user spending on public cloud services in India is separately forecast to grow 28.1% to US$ 17.5 billion in CY2026, led by infrastructure- as-a-service (up 40%) and platform-as- a-service (up 25.4%), while information security spending is projected to reach US$ 3.4 billion (up 11.7%), as Indian enterprises confront increasingly sophisticated AI-driven threats, including identity-based attacks and deepfake-enabled fraud, alongside the compliance requirements of the Digital Personal Data Protection (DPDP) Act.

The broader global backdrop remains supportive: worldwide IT spending is projected to reach US$ 6.31 trillion in CY2026, driven substantially by AI infrastructure and GenAI adoption, with Gartner projecting that spending on software with embedded GenAI will overtake spending on software without it during the year. For Indian technology services providers, this points to sustained demand for AI-enabled solutions, cloud migration, application modernisation and cybersecurity services, even as rising input costs (from memory to specialised AI talent) continue to create a gap between nominal and real IT budget growth for enterprise CIOs.

Source: Gartner

Emerging Trends in Information Technology Industry

Artificial Intelligence

The global AI market is estimated at roughly US$ 900 billion in CY2026 according to Precedence Research, on a trajectory toward an estimated US$ 4.2 trillion by CY2035, a CAGR of approximately 18.7% over the period. Market-sizing estimates for CY2026 vary considerably across research houses (from roughly US$ 375 billion to over US$ 600 billion, depending on scope and methodology), reflecting differences in how hardware, software, services and vertical-specific AI spend are counted, but all point toward sustained, rapid expansion. North America continues to command the largest share of the market, underpinned by concentrated AI compute capacity, deep venture and enterprise investment, and the concentration of leading foundation-model developers and hyperscalers within the region. Asia-Pacific remains the fastest- growing region, supported by sovereign AI programmes (including Indias IndiaAI Mission) and rapid enterprise digitalisation, while Europe continues to combine steady AI adoption, particularly in transportation and research applications, with a more structured regulatory environment.

Enterprise adoption has moved decisively from pilot to production during the year: industry surveys suggest that a majority of large organisations now have at least one AI workload running in production, up sharply from just a few years ago, with generative AI usage roughly doubling within many organisations over the past year. Investment continues to be capital-intensive and concentrated in AI compute infrastructure, even as a growing share of enterprise spend shifts toward user-facing applications and AI- enabled software as deployments mature.

Intelligent Document Processing (IDP)

Intelligent Document Processing (IDP) is emerging as one of the fastest-growing enterprise AI segments in India as organizations seek to automate document-intensive workflows and improve productivity. According to industry estimates, the Indian Intelligent Document Processing market was valued at approximately US$108.6 million in 2025 and is expected to grow at a CAGR of approximately 33% through 2034 to reach USD 1,509.0 Million. The growth will be driven by increasing adoption across banking, insurance, healthcare, retail and enterprise services. Organizations are increasingly leveraging AI-powered platforms to automate document classification, data extraction, contract review, compliance workflows and business process automation, reducing manual effort while improving accuracy and operational efficiency. These trends support the longterm growth potential of enterprise document intelligence platforms such as DocuXray.

Financial Services

The global market for generative AI in financial services is estimated at roughly US$ 2.5 billion in CY2026, up from around US$ 2 billion in CY2025, and is projected to reach approximately US$ 17.9 billion by CY2035, a CAGR of nearly 25%, according to Precedence Research. Automation of back-office functions — transaction processing, reconciliation, compliance and fraud detection — has become standard practice at most large financial institutions during the year, with the most AI-mature institutions now moving toward more autonomous decisionmaking and real-time risk assessment, even as this raises fresh questions around data privacy and model governance.

Indian BFSI Digital Transformation and Document Intelligence Opportunity

Indias banking, financial services and insurance (BFSI) sector is witnessing rapid digital transformation driven by increasing adoption of artificial intelligence, automation and regulatory technology solutions. Financial institutions are increasingly investing in intelligent document processing, automation of customer onboarding, KYC verification, loan origination, claims processing and compliance management to improve operational efficiency and reduce turnaround times. Simultaneously, the implementation of the Digital Personal Data Protection (DPDP) framework and rising focus on governance and information security are encouraging enterprises to adopt secure and scalable document intelligence platforms capable of processing large volumes of structured and unstructured data. These trends are creating significant opportunities for AI-powered document automation and enterprise knowledge management solutions across the Indian BFSI ecosystem.

AI Trends in Financial Services

1 Hyper-Personalized Customer Experiences

Financial institutions are increasingly leveraging AI and machine learning to deliver highly personalized banking and investment experiences. By analyzing customer behavior, spending patterns, and life events, AI can provide tailored product recommendations, proactive financial advice, and customized service offerings, enhancing customer satisfaction and loyalty.

2 Generative AI Transforming

2 Financial Operations

Generative AI is revolutionizing financial services by streamlining document generation, reporting, research, underwriting, risk assessment, and advisory services. The technology enables faster analysis of large datasets and supports better decision-making, helping financial institutions improve productivity, efficiency, and service delivery.

3 Rise of Agentic AI and Autonomous Banking

A significant emerging trend is the adoption of Agentic AI, which goes beyond traditional chatbots and automation tools. These intelligent systems can autonomously monitor transactions, execute complex workflows, detect anomalies, and continuously learn from data. Agentic AI is expected to improve operational efficiency, enhance customer engagement, and support real-time decision-making across financial institutions.

4 Advanced Fraud Detection and Cybersecurity

AI is playing an increasingly important role in protecting financial institutions against fraud and cyber threats. Advanced AI-driven systems can analyze vast volumes of transactions in real time, identify suspicious patterns, strengthen anti-money laundering (AML) processes, and enhance Know Your Customer (KYC) compliance. The integration of AI with behavioral analytics and deepfake detection is expected to further strengthen financial security frameworks.

5 AI-Powered Risk Management and Compliance

Financial institutions are utilizing AI to improve risk assessment, regulatory compliance, and governance frameworks. Intelligent systems can process large and complex datasets, identify emerging risks, monitor compliance requirements, and support more accurate forecasting, enabling institutions to navigate an increasingly complex regulatory environment.

6 Sustainable Finance and ESG Integration

AI is becoming a valuable tool in advancing sustainability initiatives across the financial sector. From carbon footprint monitoring and ESG data analysis to sustainable investment recommendations, AI enables greater transparency, improved reporting accuracy, and more informed decision-making. These capabilities are expected to play an increasingly important role as sustainability regulations continue to evolve.

7 Expansion of Open Banking Ecosystems

The continued growth of open banking is creating a more connected financial ecosystem. Through secure APIs and data-sharing frameworks, AI can deliver real-time insights, personalized financial recommendations, and seamless customer experiences across banking, payments, lending, insurance, and investment services.

8 Strengthening Human-AI Collaboration

As AI capabilities expand, financial institutions are increasingly focusing on combining human expertise with AI-driven insights. While AI enhances speed, accuracy, and scalability, human oversight remains essential to ensure ethical governance, transparency, regulatory compliance, and responsible decision-making.

Outlook

The future of financial services will be shaped by enterprise-wide AI adoption, with organizations leveraging intelligent technologies to enhance customer experiences, strengthen risk management, improve operational efficiency, and create new growth opportunities. Institutions that successfully balance innovation with governance and responsible AI practices will be best positioned to thrive in an increasingly digital and data-driven financial landscape.

Senior Living and AgeTech

Opportunity in India "

India is undergoing a significant demographic transition, creating new opportunities across the senior living and eldercare ecosystem. According to industry estimates, Indias senior population is expected to increase from approximately 162 million in 2025 to more than 191 million by 2030, while organized senior living adoption remains at an early stage with market penetration of only around 1.4%.

Growing life expectancy, changing family structures, urbanization and increasing demand for professionally managed senior living communities are driving the expansion of the sector. The senior living market is expected to witness substantial growth over the coming years, creating demand for technology-enabled solutions to manage resident engagement, operations, healthcare coordination and service delivery. These long-term structural trends provide a strong foundation for AgeTech platforms such as RELY that are designed specifically for operators across the senior living, assisted living and home-care ecosystem.

Manufacturing ^

The global market for AI in manufacturing is estimated at approximately US$ 12.4 billion in CY2026, up from US$ 8.6 billion in CY2025, and is projected to reach roughly US$ 287 billion by CY2035, reflecting a CAGR of over 42%, according to Precedence Research. This continued acceleration reflects AIs deepening role in Industry 4.0 initiatives (smart automation, predictive maintenance, and data-driven decisionmaking) as manufacturers move beyond pilot deployments toward plant-wide adoption. AI-driven computer vision and object detection continue to transform quality control and defect detection on high-speed production lines, reducing reliance on manual inspection and enabling real-time verification of labelling, dimensions and structural integrity.

AI Trends in Manufacturing

Predictive Maintenance and Reduced Downtime

Manufacturers are increasingly leveraging AI-powered predictive maintenance solutions to monitor equipment health in real time and identify potential failures before they occur. By analyzing machine and sensor data, AI enables proactive maintenance scheduling, reducing unplanned downtime, extending asset life, and improving overall equipment effectiveness.

Smart Quality Control through

2 Computer Vision

AI-driven computer vision systems are enhancing quality assurance by detecting defects and anomalies with greater accuracy than traditional inspection methods. Real-time visual inspection helps improve product consistency, reduce waste, and minimize human error, resulting in higher quality standards across manufacturing processes.

Connected Factories and 3 Industrial IoT Integration

The convergence of AI with Industrial Internet of Things (IIoT) technologies is driving the rise of smart factories. Connected machines, sensors, and production systems generate real-time data, enabling AI-powered insights that optimize production, improve resource allocation, and enhance operational visibility across the value chain.

4 Digital Twins for Real-Time Simulation and Optimization

Digital twin technology is gaining momentum as manufacturers create virtual replicas of machines, production lines, and facilities. AI-powered digital twins allow companies to simulate operations, identify bottlenecks, test process improvements, and optimize performance before implementing changes in the physical environment.

5 Human-AI Collaboration and Smart Robotics

The next generation of manufacturing will be characterized by closer collaboration between humans and intelligent machines. AI-enabled robots and collaborative robots (cobots) are increasingly supporting workers by performing repetitive tasks, improving workplace safety, and enhancing productivity while allowing employees to focus on higher-value activities.

AI-Driven Supply Chain and Demand Forecasting

AI is improving supply chain resilience by enabling accurate demand forecasting, inventory optimization, and logistics planning. Advanced analytics help manufacturers respond quickly to changing market conditions, minimize inventory costs, and ensure seamless operations across global supply networks.

Edge AI and Real-Time Decision Making

The adoption of edge computing is bringing AI capabilities closer to production environments. By processing data at the source, manufacturers can make faster decisions, reduce latency, improve system reliability, and enable real-time optimization of manufacturing processes.

Sustainable and Energy-Efficient Manufacturing

AI is emerging as a key enabler of sustainable manufacturing. Intelligent systems help optimize energy consumption, reduce material waste, improve resource utilization, and lower carbon emissions. As sustainability becomes a strategic priority, AI will play an increasingly important role in helping manufacturers achieve environmental goals

Outlook

Looking ahead, AI will continue to drive the evolution of Industry 4.0 by enabling autonomous operations, smarter factories, and more agile production systems. Organizations that embrace AI-driven manufacturing technologies will be better positioned to improve efficiency, enhance product quality, accelerate innovation, and maintain a competitive advantage in an increasingly dynamic market environment.

Aerospace

The global market for AI in aerospace and defense is estimated at roughly US$ 28.0 billion in CY2026, up from US$ 25.4 billion in CY2024, and is projected to reach approximately US$ 65.4 billion by CY2034, a CAGR of around 9.9%, according to Precedence Research; other estimates that include the fastest-growing generative AI and software sub-segments point to an even steeper trajectory. AI continues to accelerate design cycles ahead of manufacturing and deployment, spanning generative design tools that optimise component weight and structure, AI- driven simulation and digital-twin technologies that reduce reliance on physical prototypes, and predictive analytics that monitor tool and part wear on the factory floor.

AI Trends in Aerospace

AI-Powered Predictive Maintenance

Aerospace companies are increasingly utilizing AI and machine learning to monitor aircraft systems and predict equipment failures before they occur.

By analyzing data from sensors and onboard systems, AI helps optimize maintenance schedules, reduce unscheduled downtime, improve fleet reliability, and lower operating costs. Industry studies indicate that AI-enabled maintenance programs can reduce unscheduled maintenance events by up to 30%.

O Intelligent Flight Operations and Fuel Optimization

AI is helping airlines improve operational efficiency through advanced flight planning and route optimization. By analyzing weather conditions, air traffic patterns, and aircraft performance data, AI enables more efficient flight paths, reducing fuel consumption, lowering emissions, and improving overall operational economics.

?Autonomous and Assisted Aviation Systems

The aerospace industry is making significant progress toward autonomous flight capabilities. AI supports real-time decision-making, navigation, and situational awareness, enhancing both autonomous and semi-autonomous aircraft operations. These technologies are expected to play an increasingly important role in unmanned aerial systems, defense applications, and next- generation commercial aviation.

OAI-Driven Manufacturing and Quality Control

Aircraft manufacturers are adopting AI-powered computer vision and machine learning solutions to improve production efficiency and quality assurance. For example, AI-enabled visual inspection systems can automatically identify assembly issues, track production activities, and improve manufacturing precision while minimizing human error.

?Enhanced Decision-Making Through Data Analytics

Modern aircraft generate vast amounts of operational data. AI enables aerospace organizations to analyze these complex datasets, uncover hidden patterns, improve operational planning, and support faster, more informed decision-making across maintenance, flight operations, and supply chain management.

6 Integration with Advanced Air Mobility

AI is expected to play a foundational role in the development of Advanced Air Mobility (AAM) solutions, including electric vertical take-off and landing (eVTOL) aircraft. Intelligent flight management systems, autonomous navigation, and real-time traffic coordination will be essential for enabling safe and scalable urban air mobility networks.

7 AI-Enabled Aerospace Ecosystems

The combination of AI, machine learning, digital twins, and connected aerospace platforms is creating more resilient and efficient aerospace ecosystems. AI supports predictive modeling, system simulations, operational optimization, and improved coordination between aircraft, maintenance teams, airports, and air traffic management systems.

8 Accelerating Aerospace Innovation

The aerospace AI market is expected to witness strong growth over the coming years as organizations invest in intelligent technologies to enhance safety, operational performance, and sustainability. AI is increasingly being viewed as a strategic enabler for future aerospace innovation, supporting advancements across commercial aviation, defense, and space exploration..

Cybersecurity

The global market for AI in cybersecurity is estimated at approximately US$ 35.4 billion in CY2026, up from US$ 29.6 billion in CY2025, and is projected to reach roughly US$ 168 billion by CY2035, a CAGR of nearly 19%, according to Precedence Research, with demand continuing to be driven by the expansion of cloud-based security needs, 5G rollouts, and a rapidly evolving threat landscape that increasingly includes AI-generated attacks themselves. AI-powered surveillance and digital-monitoring systems continue to move beyond passive recording toward active, real-time identification of anomalies, unauthorised access and suspicious behaviour, both physically and online.

AI Trends in Cybersecurity

Al-Powered Threat Detection and Response

AI is transforming cybersecurity by enabling real-time threat detection and automated incident response. Machine learning algorithms continuously analyze network traffic, user behavior, and system activity to identify anomalies and potential threats faster than traditional security tools. This allows organizations to detect and contain cyber incidents before they cause significant damage.

Rise of Al-Driven Cyberattacks

Generative AI is lowering the barriers for cybercriminals by enabling the creation of highly convincing phishing campaigns, automated vulnerability assessments, and deepfake-based social engineering attacks. AI-generated phishing emails can mimic human communication styles, making them significantly harder to identify and increasing the importance of advanced defense mechanisms.

A Advanced Phishing and Fraud Prevention

Organizations are increasingly deploying AI-enabled detection systems that leverage Natural Language Processing (NLP) and behavioral analytics to identify fraudulent communications and phishing attempts. These systems can analyze language patterns, message intent, and suspicious activities to prevent attacks before they reach end users.

4 Predictive and Intelligence-Led Security

AI is enhancing threat intelligence capabilities by correlating data across multiple sources, industries, and geographies. Advanced analytics help identify emerging attack patterns, uncover coordinated cyber campaigns, and enable organizations to proactively strengthen their security posture before threats materialize.

Cloud Security Enhancement

As businesses continue migrating to cloud environments, AI is playing an increasingly important role in protecting cloud infrastructure. Intelligent security solutions can detect misconfigurations, identify unusual access patterns, monitor sensitive data usage, and strengthen security across hybrid and multicloud environments.

Automated Security Operations

The global shortage of cybersecurity professionals is accelerating the adoption of AI- driven automation. Security Operations Centers (SOCs) are increasingly utilizing AI to reduce alert fatigue, automate investigations, prioritize risks, and streamline incident management processes. Automation enables security teams to respond faster and operate more efficiently despite resource constraints.

AI-Powered Behavioral Analytics

Behavioral analytics is becoming a key cybersecurity capability. AI systems establish baselines of normal user and system behavior and can quickly identify deviations that may indicate insider threats, compromised accounts, or previously unknown attack methods. This approach enables earlier detection and more accurate threat identification.

AI-EnhancedCyber Forensics

Artificial intelligence is significantly improving cyber forensics and incident investigations. AI- powered tools can rapidly analyze large volumes of security data, reconstruct attack paths, identify root causes, and accelerate recovery efforts. These capabilities help organizations improve resilience and strengthen future defenses.

Strengthening Identity and Access Security

Identity is becoming the new security perimeter in a Zero Trust environment. AI is supporting continuous authentication, intelligent identity verification, and access monitoring to reduce the risk of credential compromise and unauthorized access. Organizations are increasingly adopting identity-centric security frameworks supported by AI-driven analytics.

Ck Responsible AI Governance and

Regulatory Compliance

As AI adoption increases, organizations are placing greater emphasis on AI governance, transparency, and regulatory compliance. Firms are investing in explainable AI, bias mitigation, and robust monitoring frameworks to ensure cybersecurity solutions remain effective, ethical, and aligned with emerging regulatory requirements.

Machine Learning

The global machine learning market is estimated at approximately US$ 126.9 billion in CY2026, up from US$ 94.0 billion in CY2025, and is projected to reach roughly US$ 1.7 trillion by CY2035, a CAGR of nearly 34%, according to Precedence Research. Machine learning continues to be a foundational technology across healthcare, finance, education and the broader AI ecosystem, with AI-augmented software development (including AI coding assistants) continuing to mature and reduce development time across industries.

AI Trends in Machine Learning

1 Rise of Agentic AI and Autonomous Decision-Making

One of the most significant trends is the emergence of agentic AI systems that move beyond providing recommendations to executing actions and managing workflows autonomously. Organizations are increasingly deploying AI solutions that can complete tasks, optimize processes, and support operational decisions with minimal human intervention.

2 Convergence of Generative AI and ² Machine Learning

Generative AI is increasingly being integrated with traditional machine learning models to enhance content creation, research, reporting, software development, and business decision-making.

This convergence enables organizations to unlock greater value from enterprise data while improving productivity and accelerating innovation.

3 Industry-Specific AI Models

Organizations are shifting from generic AI solutions toward domain-specific and industry-focused machine learning models. These specialized systems are trained on sector-specific datasets, enabling more accurate predictions, better operational outcomes, and greater business relevance across industries such as healthcare, finance, manufacturing, and aerospace.

4 Edge AI and Real-Time Intelligence

The deployment of machine learning models at the edge is gaining momentum, allowing data to be processed closer to its source. Edge AI enables faster decision-making, reduced latency, improved privacy, and enhanced operational efficiency, particularly in IoT-enabled environments and realtime applications.

5 Multimodal AI Systems

The next generation of machine learning models can simultaneously process multiple forms of data, including text, images, audio, video, and structured datasets. These multimodal capabilities provide deeper contextual understanding and enable more sophisticated business applications, enhancing user experiences and decision-making capabilities.

6 Human-AI Collaboration

While automation continues to expand, organizations are increasingly focusing on human- AI collaboration rather than full replacement. AI systems are being designed to augment human capabilities, enabling employees to focus on strategic initiatives, innovation, and complex problem-solving while AI handles repetitive and data-intensive tasks.

7 Explainable and Responsible AI

As AI assumes greater responsibility in business processes, transparency, governance, and ethical AI practices are becoming increasingly important. Organizations are investing in explainable AI frameworks to improve trust, reduce bias, ensure compliance, and strengthen accountability in AI- driven decision-making.

8 Operationalization of AI at Scale

AI is moving beyond pilot programs into enterprisewide deployment. Businesses are increasingly integrating machine learning into core operations, enabling automated workflows, predictive analytics, intelligent process management, and data-driven decision-making across the organization.

Metaverse

The global metaverse market is estimated at approximately US$ 263.8 billion in CY2026, up from US$ 183.2 billion in CY2025, and is projected to reach roughly US$ 3.1 trillion by CY2035, a CAGR of nearly 33%, according to Precedence Research. Decentralised virtual economies built on blockchain, digital assets and NFTs continue to underpin trust and ownership models within virtual environments, while AI-powered avatars and virtual assistants are becoming increasingly capable of personalised, contextual interaction.

AI Trends in Metaverse

AI-Generated Virtual Worlds

Generative AI is revolutionizing the creation of virtual environments by enabling the automatic generation of 3D spaces, objects, and interactive experiences through text and voice prompts.

This significantly reduces development time and allows organizations to build highly customized and dynamic virtual worlds at scale

2 Intelligent Digital Avatars

AI-powered avatars are becoming increasingly sophisticated, capable of understanding natural language, displaying realistic emotions, and interacting with users in a human-like manner. These intelligent avatars are enhancing collaboration, customer engagement, training, and social experiences within virtual environments.

3 Personalized Immersive Experiences

Machine learning algorithms are enabling highly personalized metaverse experiences by analyzing user behavior, preferences, and interactions. AI can dynamically adapt content, environments, and recommendations, creating more engaging and relevant experiences for individual users.

4 Spatial Computing and Natural Interfaces

The convergence of AI with spatial computing is transforming how users interact with digital content. Voice commands, gesture recognition, eye tracking, and contextual awareness are enabling more intuitive and seamless interactions between physical and virtual environments.

5 Industrial Metaverse and Digital Twins

Enterprises are increasingly leveraging AI- powered digital twins and simulated environments to optimize operations, improve product design, enhance employee training, and support predictive maintenance. The industrial metaverse is emerging as one of the most significant commercial applications of immersive technologies.

6 AI-Driven Virtual Economies

AI is supporting the development of intelligent virtual economies by automating asset management, transaction monitoring, pricing mechanisms, and digital commerce. These capabilities are improving efficiency, scalability, and user engagement within virtual marketplaces and metaverse platforms.

7 Enhanced Collaboration and Learning

Organizations are increasingly utilizing AI-enabled metaverse platforms for remote collaboration, immersive training, and knowledge-sharing experiences. Virtual environments supported by AI can simulate real-world scenarios, improve employee engagement, and accelerate learning outcomes.

?Real-Time Simulation and Intelligent Environments

AI enables virtual worlds to respond dynamically to user actions and environmental changes. Realtime simulations, behavioral intelligence, and adaptive environments create more realistic and interactive experiences, improving engagement and operational value across metaverse applications.

Business Overview

Xelpmoc Design and Tech Limited is a technology-driven company providing consulting, software development, and digital transformation solutions to enterprises, startups, and government organizations.

The Company specializes in next-generation Artificial Intelligence (Al), Machine Learning (ML), Natural Language Processing (NLP), and Data Analytics, helping clients leverage data and technology to improve operational efficiency, decision-making, and business outcomes.

The Company partners with a diverse set of stakeholders, including governments, businesses, startups, and individuals, delivering innovative technology solutions tailored to their evolving needs. Through its wholly owned subsidiary, Signal Analytics Private Limited, Xelpmoc continues to expand its capabilities across emerging technology domains and strengthen its service offerings.

As part of its long-term growth strategy, the Company is increasingly focused on building and scaling a technology-led product portfolio while continuing to expand its customer base across domestic and international markets. The Company continues to engage with prospective customers and industry participants regarding its Al-enabled solutions and digital platforms.

Xelpmoc offers a broad range of technology products, platforms, managed services, and consulting solutions. The Companys focus remains on developing innovative, scalable, and industry-specific solutions across sectors such as LegalTech, Banking, Financial Services and Insurance (BFSI),

Real Estate, and ElderCare. Its integrated technology platform enables efficient resource utilization, faster deployment cycles, and lower development and maintenance costs, creating long-term value for customers.

FY26 marked an important phase in the Companys strategic evolution. Against a challenging startup funding environment, Xelpmoc strengthened its focus on enterprise customers, data science-led engagements and proprietary technology platforms. The Company continued to invest in product development, with particular emphasis on building scalable Al-driven solutions capable of addressing industry-specific challenges. This strategic direction reflects Xelpmocs long-term objective of creating sustainable and technology-led growth opportunities through a balanced combination of enterprise solutions, proprietary products and startup partnerships.

Product Portfolio

DocuXray - Enterprise Document Intelligence Platform

During FY26, the Company continued the development of DocuXray, an Al-powered document intelligence platform designed to help enterprises process, analyse and derive actionable insights from large volumes of structured and unstructured documents.

Built as the next evolution of the Companys earlier document-processing capabilities, DocuXray combines Artificial Intelligence, Machine Learning and advanced data analytics to automate document-intensive workflows, improve information accessibility and enhance operational efficiency.

The platform has been designed to support enterprise use cases across industries such as Banking, Financial Services and Insurance (BFSI), Legal Services, Real Estate and Compliance-driven businesses, where document processing remains a critical operational requirement. As organizations increasingly seek to leverage Al while maintaining greater control over sensitive enterprise information, DocuXray is focused on enabling secure, scalable and business- context driven document intelligence solutions.

The Company believes the growing adoption of Artificial Intelligence, increasing compliance requirements, digital transformation initiatives and the need for automation of document-centric processes are creating significant long-term opportunities in this segment. Going forward, Xelpmoc intends to continue enhancing DocuXrays capabilities and positioning the platform as a key component of its enterprise Al and data intelligence offerings.

FY26 was primarily focused on product development, capability enhancement and market validation initiatives for the platform.

RELY - AgeTech Platform for the Senior Living Ecosystem

During FY26, the Company introduced RELY and continued its development as an AgeTech platform platform designed to support operators across the senior living, assisted living and home-care ecosystem. The platform has been developed to address the growing technology needs of senior care providers by enabling improved operational management, resident engagement and ecosystem-wide coordination through a unified digital platform.

Increasing life expectancy, changing family structures and growing demand for organized senior care services are driving the evolution of the senior living industry both in India and globally. RELY has been conceptualized to address this structural opportunity by providing technology infrastructure tailored to the unique requirements of elder-care operators and service providers. The platform is designed to support operational workflows while creating the foundation for a broader connected ecosystem of stakeholders across the senior care value chain.

Tine launch of RELY represents an important milestone in Xelpmocs strategy of building sector-focused technology platforms with long-term scalability potential. The Company intends to continue strengthening the platforms capabilities, expanding its solution stack and further enhancing its relevance for operators in the rapidly evolving senior living and care industry.

FY26 represented the initial launch and platform-building phase for RELY, with the Company focusing on refining product capabilities and evaluating market opportunities within the senior living ecosystem

Enterprise Al and Digital Transformation Solutions

The Company provides enterprise-focused solutions across Artificial Intelligence (Al), Machine Learning (ML), Data Science and advanced analytics. Leveraging its technology expertise, Xelpmoc helps organizations automate workflows, improve decision-making, enhance operational efficiency and unlock value from enterprise data. The Companys solutions are designed to address complex business challenges across industries through intelligent automation, analytics-driven insights and scalable digital transformation initiatives.

LegalTech and BFSI Solutions

Building on its expertise in Al and document intelligence, the Company develops technology solutions for legal and financial services organizations. These solutions support applications such as document automation, contract review, compliance management, case management, intelligent document processing and workflow automation. By reducing manual intervention and improving accuracy, these solutions help enterprises enhance operational efficiency, strengthen governance and streamline business processes.

FY26 Financial Performance Snapshot

Total operating revenue was Rs. 37.4 million in FY26 as compared to Rs. 39.0 million in FY25. Adjusted Operating EBITDA was Rs. (63.0) million in FY26 as compared to Rs. (73.2) million in FY25. The fair value of the Companys portfolio investments as on March 31, 2026, stands at 726.8 million as compared to 631.8 million as on March 31, 2025. Xelpmoc has cumulatively served 65 clients till FY26 as compared to 64 clients till FY25. The Company has a strong and qualified team of 63 members.

Highlights of the year ended March 31,2026

During the year FY26, the Company successfully executed the partial divestment of its investment in Mihup Communications Private Limited ("Mihup") in line with the approved Share Purchase Agreement. The transaction involved the transfer of 11,782 Series Seed CCPS of the face value of Rs. 1/- per share at a price of Rs. 8487.32/- per share aggregating to total consideration of Rs. 100 million. The transaction has been successfully completed, and the consideration amount has been duly received by the Company, strengthening liquidity and validating the value created through the investment.

Our Competitive Strengths

Comprehensive Technology Solutions

The Company offers a broad portfolio of technology-led products, services, and solutions, including managed services, platform development, system integration, and advanced analytics capabilities. Over the years, the service portfolio has expanded to encompass artificial intelligence, data science, query optimization, and rapid product development solutions. These capabilities enable the Company to support businesses at various stages of their growth journey, from product conceptualization to scale-up.

Deep Domain Expertise

The Company benefits from the extensive industry experience of its leadership team and founders, who bring expertise across sectors such as financial services, retail, media, and technology. This is further complemented by a network of independent industry specialists and consultants who provide strategic guidance and domain-specific insights. The combined expertise allows the Company to deliver technology solutions that are aligned with evolving business requirements and industry best practices.

Client-Centric Delivery Model

The Companys organizational structure is designed around solution-oriented service lines, enabling clients to seamlessly access specialized expertise across multiple domains. By fostering long-term, relationship-driven engagements, the Company focuses on delivering sustained value to its clients. Its integrated delivery platform facilitates efficient utilization of resources and capabilities, helping reduce development and maintenance costs while ensuring scalable and high-quality outcomes.

Experienced Leadership and Innovation-Driven Culture

The Company is led by an experienced management team with leadership backgrounds across diverse industries and organizations. Their collective expertise contributes to a culture that encourages innovation, collaboration, agility, and continuous learning. The Company also invests in talent development initiatives aimed at nurturing future leaders and strengthening organizational capabilities to support long-term growth.

Growth Strategy

Product-Led Growth Strategy

The Company is increasingly focused on developing and scaling proprietary technology platforms that address specific industry challenges. Through DocuXray and RELY, Xelpmoc is building differentiated solutions in the enterprise document intelligence and AgeTech segments. These platforms represent strategic investments aligned with longterm industry trends and the Companys objective of building scalable and technology-led growth opportunities. The Company will continue to enhance product capabilities and evaluate commercialization opportunities while leveraging its expertise in Artificial Intelligence, Machine Learning and Data Science.

Expanding Corporate Engagements

The Company has successfully delivered technology solutions addressing a range of business challenges, including data analytics, fraud detection, digital transformation, application modernization, and supply chain optimization. Building on these engagements, the Company aims to deepen existing client relationships and pursue larger, higher-value opportunities across industries.

Strengthening Startup Partnerships

The Company continues to partner closely with startups by acting as a technology and innovation partner throughout their growth journey. In addition to supporting product development and technology architecture, the Company helps startups build market access and enterprise relationships. This collaborative approach enables startups to scale their solutions while creating long-term value for all stakeholders.

Enhancing Domain and Technology Capabilities

The Company remains committed to investing in emerging technologies, including Artificial Intelligence, Machine Learning,

Data Science, and Deep Learning. Continued focus on capability building, strategic talent acquisition, and innovation-driven product development is expected to strengthen its market position and enhance its ability to address evolving customer requirements.

Expanding Global Reach

The Company continues to explore opportunities across international markets through its global presence and strategic partnerships. Its overseas operations provide access to emerging opportunities across regions, enabling the Company to broaden its customer base, enhance market visibility, and support long-term business growth.

Financial Performance

Consolidated Financial Highlights

								(Rs. in Million)
Particulars	Q4 26	Q3 26	QoQ%	Q4 25	YoY%	FY 26	FY 25	YoY%
Revenue from Operations	10.80	11.20	(3.2)	7.10	52.8	37.4	39.0	(4.2)
Other Income	1.8	2.0	(9.3)	1.1	60.7	7.3	9.0	(19.7)
Total Income	12.6	13.2	(4.2)	8.2	53.9	44.6	48.0	(7.1)
Adjusted Operating EBITDA	(15.6)*	(14.9)*	NA	(15.3)*	NA	(63.2)*	(73.2)*	NA
% of Operating Revenue	NA	NA	NA	NA	NA	NA	NA	NA
PAT	(17.8)	(20.0)	NA	(18.4)	NA	(80.7)	(80.7)	NA

*Adjusted Operating EBITDA is after excluding ESOP expenses 0.1 millionRs.0.9 millionRs.2.5 millionRs.2.5 million and 2.4 million during Q4FY25, Q1FY26, Q2FY26, Q3FY26 and Q4FY26 respectively

Ratios - FY26 Consol Excel

				(Rs. in Million)
Sr. No. Ratio	31st March 2026	31st March 2025	% Change	Reasons for Variance
1 Current Ratio	5.02	3.67	37%	Increase due to investment in mutual funds during the year.
2 Debt-Equity Ratio	-	-	0%	Debt Free Company
3 Return on Equity Ratio	(0.11)	(0.13)	-15%	Loss during the year
4 Inventory turnover ratio	N.A.	N.A.	N.A.	No Inventory
5 Debtors turnover ratio	3.55	3.72	-5%	Decrease due to lower net credit sales, which was not proportionately offset by the reduction in average trade receivables.
6 Net Profit Margin	(2.03)	(2.07)	-2%	The ratio improved primarily due to a reduction in net loss during the year, despite a marginal decline in net sales.

Outlook

Looking ahead, the Company will continue to strengthen its focus on Artificial Intelligence (AI), Machine Learning (ML), Data Science and advanced analytics while accelerating the commercialization of its proprietary platforms, DocuXray and RELY. Management believes the increasing adoption of AI-enabled automation, intelligent document processing and digital transformation solutions will continue to create opportunities across enterprise customers.

The Company intends to deepen its engagement with corporate clients, expand product capabilities and pursue growth opportunities in sectors such as BFSI, LegalTech, Real Estate and ElderCare. Through continued investment in innovation, strategic partnerships and customer- focused product development, Xelpmoc aims to strengthen its position as a provider of industry- specific technology platforms and enterprise AI solutions.

While maintaining a selective approach towards startup investments, the Company remains committed to supporting high-potential ventures that align with its long-term technology vision. With a growing product portfolio, strong technology foundation and disciplined capital allocation strategy, Xelpmoc believes it is well-positioned to participate in emerging opportunities across the evolving digital economy.

Risk Management

The Board of Directors regularly reviews the Companys risk profile and oversees the implementation of appropriate risk management frameworks. Supported by the senior management team, the Company continuously assesses potential risks and undertakes suitable mitigation measures to safeguard business continuity and long-term value creation.

Kev Business Risks and Mitigation Measures

Market Risk

The Companys performance may be influenced by changes in macroeconomic conditions, geopolitical developments, regulatory changes, and fluctuations in industry demand. Adverse developments in domestic or global markets could impact business growth and customer spending. To mitigate such risks, the Company continues to diversify its client portfolio, expand its industry presence, and explore opportunities across multiple geographies and sectors.

The technology services industry remains highly competitive, with the emergence of new entrants and evolving customer expectations. Sustained competitiveness requires continuous innovation, deep domain expertise, and the ability to deliver differentiated solutions. The Company leverages its strengths in Artificial Intelligence, Machine Learning, Data Analytics, and domain-specific technology solutions to deliver value-added services and maintain long-term client relationships.

Rapid advancements in technology, changing business models, and evolving customer requirements necessitate continuous innovation and adaptability. The Companys ability to remain relevant depends on its capacity to develop and deploy effective technology solutions. To address this risk, the Company continues to invest in emerging technologies, strengthen its product portfolio, and enhance its technical capabilities to meet evolving market demands.

Talent Risk

The availability and retention of skilled technology professionals remain critical to the Companys success. Increasing competition for specialized talent in areas such as AI, Machine Learning, and Data Science may impact growth. The Company addresses this risk by fostering an inclusive and entrepreneurial work culture, providing opportunities for learning and professional development, and offering competitive employee engagement and retention programs.

Human Resources

The Company recognizes that its employees are among its most valuable assets and play a critical role in driving innovation, customer satisfaction, and sustainable growth. Xelpmoc remains committed to creating a supportive work environment that encourages professional development, continuous learning, and high performance.

The Companys human resource strategy focuses on attracting, developing, engaging, and retaining talented professionals through structured learning initiatives, skill enhancement programs, and career development opportunities. Emphasis is placed on cultivating a workplace culture built on diversity, inclusion, collaboration, creativity, integrity, and mutual respect.

The Company also promotes employee engagement through various people-centric initiatives, recognition programs, and performance-driven reward mechanisms aimed at fostering a motivated and high- performing workforce.

As of March 31, 2026, the Company had a team of 63 members comprising employees, consultants, and interns who collectively contribute to the achievement of its strategic objectives.

Internal Controls and Their Adequacy

The Company has established an adequate system of internal controls designed to safeguard assets, ensure operational efficiency, maintain compliance with applicable laws and regulations, and support the accuracy and reliability of financial reporting.

An independent firm of Chartered Accountants conducts internal audits on a periodic basis, and the findings are reviewed by the Audit Committee of the Board. The internal audit process evaluates the effectiveness of operational controls, financial processes, risk management practices, and compliance mechanisms across the organization.

The Company has also implemented structured review mechanisms, including periodic business performance reviews and management oversight processes, to monitor operational effectiveness and facilitate timely corrective actions wherever required.

A well-defined framework for monitoring capital expenditure and project execution is in place to ensure prudent resource allocation, budgetary discipline, and timely completion of approved initiatives. Regular updates on internal audit observations, corrective actions, and control enhancements are presented to the Audit Committee and senior management for review.

The Audit Committee periodically reviews quarterly, half-yearly, and annual financial statements, along with key internal control and compliance matters. During the year, the Company conducted a comprehensive review of its internal financial controls and found them to be adequate and operating effectively. Recommendations arising from such reviews were suitably evaluated and implemented wherever necessary.

The Company continues to strengthen its governance framework through periodic updates to its policies, procedures, and Standard Operating Procedures (SOPs) to ensure alignment with evolving business requirements, regulatory developments, and industry best practices.