Welcome to the UnifyApps Newsroom

Ragy Thomas is Chairman, Co-CEO and Co-Founder of UnifyApps , and Founder and Chairman at Sprinklr. Author - “The Enterprise Brain.” CIOs are continually asking me the same two questions: What is AI actually costing us, and is it delivering measurable ROI? The numbers explain the anxiety. According to Gartner , “72% of CIOs reported that their organizations are breaking even or are losing money on their AI investments.” Gartner also forecasts that “over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs.” AI isn’t getting expensive because intelligence itself costs more. It is getting expensive because most businesses bolt AI onto fragmented architecture. Every new agent rebuilds context, integrations, controls and verification around itself, while workflows become hard-wired to individual models. That same fragmentation makes ROI harder to prove because costs, decisions and outcomes remain scattered across systems. The problem is architectural. Fix the architecture and you change both sides of the ROI equation: The marginal cost of deploying AI falls, while the economic contribution of each workflow becomes easier to measure. Below are four architectural moves that can improve the unit economics of enterprise AI. ​Mandate a shared context layer. Most AI agents become expensive because they have to fetch context repeatedly from multiple systems just to answer one question. One multinational beauty company’s team saw this with their first attempt at agentic automation. The models worked and demoed well. But the agents kept breaking the moment they hit production, because each of the six point solutions they’d stitched together kept their own knowledge and governance in a silo. Every agent started cold, forcing the team to reconnect, reconcile and rebuild that context for every single new project. The answer is to centralize that context instead of rebuilding it agent by agent. Create a shared context layer that unifies enterprise knowledge, governance rules and permitted actions, so every AI workflow can draw from the same reconciled foundation rather than reconnecting to the business from scratch. Context is already prepared and shared, which cuts redundant token usage, reduces API traffic and makes responses both faster and cheaper. Shared context lowers the marginal cost of each new deployment because knowledge, permissions and business logic are built once and reused. At the same time, it also makes ROI easier to attribute by linking workflow cost, actions and outcomes in one place. ​Route work to the right model. When every step in a workflow runs on the same model, simple tasks end up carrying the cost of complex reasoning. One financial-services enterprise studied by researchers from the University of Hong Kong and Stellaris AI found that inference costs exceeded $200,000 per month even though more than 70% of queries were routine enough for smaller models. When you treat intelligence as a swappable input, models can change without rebuilding the surrounding workflow. Businesses should optimize high-value, judgment-intensive tasks for performance and optimize high-frequency, low-value tasks for cost. Where a smaller model or deterministic logic does the job, use it. This way, inference spend becomes intentional, controlled and tied to the real complexity of the work. The goal is predictable economics and model agility. ​Prioritize agent swarms over superagents. Rather than routing every task through a single, monolithic “superagent” that must hold the full context of a problem in memory and reason over it end-to-end, a swarm of specialized agents breaks the work into narrower, well-defined tasks. Each agent operates with a smaller, purpose-built context window instead of the sprawling prompt a generalist model would need to handle every possible scenario. Because these agents are only invoked when relevant, and only pass forward the specific output another agent needs, the system avoi

Every CIO I talk to has the same story. They built an AI pilot that worked beautifully in the lab, moved it to production and then it broke. They're not alone. An AI Momentum Survey from Dun & Bradstreet reveals the scale of the problem: "97 percent of organizations worldwide now report active AI initiatives, but only 5 percent say that their data is adequately ready to support them." Forty-four percent "identify privacy and compliance risks," while 38% lack integration across systems. In other words, nearly every organization is building pilots, but few have done the foundational work required for production. Enterprise AI isn't failing because models aren't powerful enough. It's failing because enterprises are trying to deploy AI solutions on top of fragmented data, disconnected systems and inconsistent governance. The gap between pilot and production is about architecture, not model capability. The 80/20 Production Gap Models are important, but they're only 20% of what makes AI work in production. The other 80% is scaffolding: the infrastructure connecting systems, standardizing data, orchestrating workflows, enforcing governance, managing approvals and ensuring security and compliance. I saw this when one CIO's team piloted an AI procurement agent, and the model worked in weeks. But it didn’t make it into production after 15 months! The challenge wasn't prompting or model selection. It was connecting 14 systems, reconciling inconsistent vendor data, implementing approval workflows, creating stakeholder-specific experiences and satisfying audit requirements. The model was the easiest part. The constraint was architecture. The Two-Part Architecture To Solve The 80% The organizations that I've seen successfully scale AI share two traits: unified foundations and a modular methodology of building AI components. ​Two architectural moves can help separate production-scale AI from perpetual pilot programs. 1. Horizontal Decoupling: Extract Context At The Enterprise Level Start by extracting knowledge, governance and actionability context out of individual applications and reconciling them into a unified, harmonized layer before you build anything on top. • Knowledge context creates a shared understanding of the business by reconciling customer, vendor, employee and asset data into a consistent view rather than leaving multiple versions scattered across systems. • Governance context applies policies, permissions, approval workflows and compliance controls consistently, regardless of which application or process AI is interacting with. • Actionability context defines what actions AI can take, in what order, across which systems and within what guardrails.​ ​​​When those contexts are unified, what I call the "enterprise brain" emerges. This isn't another application or data repository but a synthesized intelligence layer that gives AI systems a shared understanding of how the business operates. It provides a complete picture of the enterprise so teams can reason, act and automate with compounding intelligence rather than isolated fragments of information. Importantly, organizations don't need to build this all at once. Most begin with a single use case. The first implementation establishes the foundation. The second expands it. The third reuses what already exists. Over time, every deployment contributes additional context back into the enterprise brain, making future initiatives faster, simpler and more valuable. Eventually, this can result in organizations experiencing the multiplier effect of a shared intelligence foundation.​ 2. Vertical Decoupling: Separate What’s Being Done From Who Is Doing It If the first move creates unified context, the second determines how solutions evolve over time. Enterprise AI solutions often fail because workflows, agents, interfaces, integrations and models become tightly coupled. Every change requires touching everything else. A new approval process requires code changes, a new user experience requ

PwC and its strategy consulting subsidiary Strategy& have signed a Memorandum of Understanding with UnifyApps to accelerate government and enterprise adoption of artificial intelligence (AI) across the Gulf region. The collaboration brings together PwC’s and Strategy&’s sector expertise and transformation capabilities with UnifyApps’ agentic AI platform, creating a joint offering for organizations that want to adopt AI and Gen AI within their business. Headquartered in the US, UnifyApps provides a platform that helps organizations become AI-native. Its horizontal platform connects systems of record, knowledge, and activity, enabling enterprises to find data, think with AI models, and act within business workflows – boosting AI-driven ways of working. According to its own records, UnifyApps has thousands of pre-built integrations and an LLM-agnostic features that organizations can make leverage for their AI journey. “AI is at the heart of economic competitiveness, and organizations across the region need to focus on solutions that deliver tangible operational improvements . Through this collaboration with UnifyApps, we will enable organizations to embed responsible, secure and scalable AI solutions that align with national digital transformation priorities,” said Hani Zein, Partner at Strategy& . As part of the collaboration, PwC and Strategy& will provide sector expertise, regulatory insights and advisory capabilities to define AI use cases, governance frameworks and innovation roadmaps. UnifyApps will complement this with its AI-native platform, pre-built integrations and low-code tools to deliver secure, scalable AI solutions tailored to regional priorities. Zein said that the partnership will strengthens the capabilities of PwC and Strategy& in delivering end-to-end AI transformation. Meanwhile, for UnifyApps, the tie-up sees the company work with one of the region’s leading transformation partners. “Partnering with PwC and Strategy& allows us to bring our platform to organizations in a purposeful and practical way. Together, we are equipping teams to solve real challenges with tools that are intuitive, secure and built for the pace of today’s digital landscape,” noted Haitham Elkhatib, Co-Founder of UnifyApps.

UnifyApps has been named a winner in the 2026 Artificial Intelligence Excellence Awards, a recognition that underscores the platform’s growing role in helping large enterprises operationalize AI at scale. The Business Intelligence Group cited UnifyApps for moving customers beyond isolated pilots to production-grade agents, workflows, and applications that deliver quantifiable outcomes in core functions such as finance, supply chain, procurement, and HR. Over the past year, UnifyApps’ AI Agent and App Builder Platform has supported the launch of several thousand AI solutions by making data AI-ready, creating a shared knowledge layer, and tightly integrating systems of record across the enterprise. Co-CEO and Co-Founder Ragy Thomas said the company’s enterprise AI operating system architecture, combined with next-generation connectors, unified knowledge context, inherited governance, AI-first workflows, and model management, enables CIOs to build and maintain production AI in weeks instead of months, which can lower deployment costs and accelerate ROI. For executives, the award validates UnifyApps’ position as a horizontal platform for governed, scalable AI that is LLM-agnostic and designed to address the main bottleneck in AI adoption: fragmented, siloed systems that inhibit enterprise-wide deployment. According to the award organizers, UnifyApps stood out for delivering practical, accountable AI that improves budgeting speed, reconciliation cycles, inventory optimization, and workforce productivity, aligning with investor and board expectations for measurable impact rather than experimentation. Backed by WestBridge Capital, ICONIQ, and Elevation Capital, and headquartered in New York with operations across the Americas, EMEA, and India, UnifyApps is positioning its AI operating system as infrastructure for AI-native enterprises. This recognition may strengthen its credibility with CIOs, drive additional enterprise demand, and reinforce its strategy of focusing on trusted, governed AI as adoption moves from proof-of-concept to business-critical production systems worldwide.

Bengaluru, Seattle and London, March 26, 2026 Happiest Minds Technologies Limited (NSE: HAPPSTMNDS), an AI First, customer-centric digital engineering and Mindful IT company, today announced a strategic partnership with UnifyApps, the AI Agent and App Builder Platform for the Enterprise. Through this collaboration, Happiest Minds will use UnifyApps’ AI OS architecture and horizontal platform to help enterprises transform into AI-Native organizations. The partnership will enable clients to unify enterprise knowledge, actionability, and governance through an AI OS architecture that allows enterprises to rewire their business to transition GenAI initiatives from pilots to secure, scalable, production-grade deployments. UnifyApps’ LLM-agnostic design and extensive library of pre-built integrations complement Happiest Minds’ deep capabilities in Generative AI, digital engineering, cloud, data, and enterprise modernization. Together, the two organizations will empower enterprises to operationalize AI with speed, security, and measurable, production-grade ROI tied to cycle time reduction, productivity gains, and revenue acceleration. As enterprises move beyond experimentation toward enterprise-wide AI adoption, this partnership addresses a critical need: a unified platform that orchestrates AI agents across business workflows while ensuring governance, interoperability, and scalability. By combining Happiest Minds Generative AI Business Services Unit (GBS) expertise with UnifyApps AI-Native platform, customers will gain access to end-to-end AI transformation from strategy and use case identification to implementation and managed services. Praveen RP, Co-CEO, GBS, Happiest Minds Technologies, said, “We are excited to partner with UnifyApps at a time when enterprises are seeking to industrialize their AI initiatives. UnifyApps AI OS architecture aligns strongly with our AI First strategy and our focus on delivering scalable, secure, and business-aligned AI solutions. Together, we will help organizations move beyond pilots and unlock tangible value from Generative AI.” Maninder Singh, Chief Growth Officer, Happiest Minds, said, “The future of enterprise AI lies in agentic platforms that can orchestrate intelligence across systems and workflows. By combining our deep domain expertise and GenAI execution capabilities with UnifyApps AI Agent and App Builder Platform, we are uniquely positioned to help clients become truly AI-Native enterprises.” Pavitar Singh, Co-CEO, UnifyApps, said, “At UnifyApps, we believe enterprises must rewire from digital systems of record to cognitive systems of execution. Our AI OS unifies knowledge, actionability, and governance so agents can operate across real business workflows — not just dashboards. Together with Happiest Minds, we are enabling enterprises to translate AI ambition into measurable ROI.” This partnership reinforces the Happiest Minds commitment to building a strong ecosystem of AI innovators and technology partners to deliver next-generation digital transformation solutions globally. About Happiest Minds Technologies: Happiest Minds Technologies Limited (BSE, NSE: HAPPSTMNDS) is an AI First, customer-centric digital engineering company committed to delivering ‘Happiest People . Happiest Customers’ . With an integrated approach that spans from chip to cloud, Happiest Minds delivers secure and scalable solutions across product engineering, cybersecurity, analytics , and automation platforms. Happiest Minds brings purpose and precision to every engagement, helping enterprises solve complex business challenges and fast-track their digital evolution across industry sectors such as Banking,Financial Services & Insurance (BFSI), EdTech , Healthcare & Life Sciences , Hi-Tech and Media & Entertainment , Industrial, Manufacturing , Energy & Utilities , and Retail , CPG & Logistics. Happiest Minds’ innovation-led strategy is powered by deep expertise in disruptive tech including Gen AI and strategic part