Latest news with #NoSQL
Yahoo
10-06-2025
- Business
- Yahoo
MongoDB Just Beat Expectations: Why Am I Still Not Buying More?
MongoDB, Inc. (NASDAQ:MDB) popped following its Q1 FY26 earnings release, with shares surging more than 12% post-earnings. The beat-and-raise quarter came alongside stronger profitability and a bold $1 billion share buyback announcement, reigniting investor optimism in a stock that had recently struggled with macro and execution concerns. This article will cover MongoDB's business model, its competitive advantages, total addressable market (TAM), and the latest Q1 results to assess whether the recent performance supports or challenges my investment thesis. MongoDB is a leading provider of NoSQL database technology, offering a flexible, JSON-like document model instead of rigid relational tables. Its core products include MongoDB Atlas, a fully managed cloud database service, and MongoDB Enterprise Advanced, a self-managed on-premise solution for enterprises. The company's mission is to simplify and accelerate application development. By using MongoDB, developers can store and query diverse data types with ease, which speeds up project timelines and adapts to changing requirements better than traditional SQL databases. MongoDB's platform has grown into a full-fledged developer data platform that includes capabilities like full-text search, analytics, mobile data sync, and now vector search for AI applications. As of the latest quarter, over 57,100 customers utilise MongoDB's technology, reflecting its widespread adoption across various industries. In essence, MongoDB provides the plumbing behind modern applications, from web and mobile apps to IoT and AI systems, and its products aim to be the default database infrastructure for new software projects. Source: Ardentisys MongoDB's approach gives developers far more flexibility than traditional SQL databases. While traditional SQL databases follow a rigid model, where data is stored in structured tables with fixed schemas, MongoDB's NoSQL model stores data in flexible, JSON-like documents. This difference defines how quickly teams can iterate, adapt, and scale. In a SQL environment, changing the schema often requires complex migrations. MongoDB, on the other hand, allows for dynamic fields, nested arrays, and schema evolution with minimal disruption Scalability is another key distinction. Relational databases typically scale vertically, requiring more powerful hardware to handle growing workloads. MongoDB was designed to scale horizontally, distributing data across multiple nodes to support large-scale, real-time applications in cloud environments. These architectural differences make MongoDB more aligned with modern development needs, particularly in cases where speed, agility, and scalability are critical. As a result, NoSQL databases like MongoDB have become the preferred choice for a growing number of use cases, from web and mobile apps to AI and IoT platforms. With this distinction being said, MongoDB's competitive advantage stems from its modern architecture and developer-centric approach in a massive market. The database management system market is estimated to be over $85 billion in size, yet much of it still relies on decades-old relational database technology. However, the global NoSQL market size of approximately $10 billion in 2024 is still small but growing rapidly, expected to grow at a CAGR of 29.50% between 2025 and 2034. MongoDB's document model natively handles both structured and unstructured data and maps more naturally to how developers think in code. This flexibility allows companies to represent the messiness of real-world data and evolve their schemas without costly migrations. Applications built on MongoDB can iterate faster and scale more easily because the database does not require rigid schemas or complex join operations. According to management, this fundamental architectural advantage translates to faster time-to-market, greater agility, and the ability to scale without re-architecting, which is why customers increasingly entrust MongoDB with mission-critical workloads. Another pillar of MongoDB's moat is its developer mindshare and ecosystem. The company has invested heavily in making its platform accessible, from an open-source foundation to a free-tier Atlas offering and a wide array of developer tools and integrations. This approach creates a self-reinforcing dynamic: the more developers adopt MongoDB, the richer its ecosystem becomes, through community-driven documentation, integrations, and support, which in turn attracts even more developers. Over time, these network effects deepen the platform's defensibility. The lack of attracting developers was something that can define a platform's fate. A great example is Windows Phone which failed to convince developers to build for it. As Ben Thompson said, The number one reason Windows Phone failed is because it was a distant third in a winner-take-all market; this meant it had no users, which meant it had no developers, which meant it had no apps, which meant it had no users. This was the same chicken-and-egg problem that every potential smartphone competitor has faced since, and a key reason why there are still only two viable platforms. Each new generation of startups and IT projects choosing MongoDB adds to a virtuous cycle: those applications grow, require bigger paid deployments, and demonstrate MongoDB's reliability at scale, attracting even more adoption. This bottom-up adoption complements MongoDB's direct sales focus on enterprises, enabling it to grab market share in a large, under-penetrated market. Management frequently notes that MongoDB still has a relatively small fraction of the overall database market, leaving ample room for growth as organizations modernize their data infrastructure. Crucially, MongoDB's advantage is being reinforced as industry trends shift towards the company's strengths. The rise of cloud-native computing, microservices, and AI-driven applications all favor flexible, distributed data stores. MongoDB's platform was built for cloud, distributed, real-time, and AI-era applications, whereas many competitors are now scrambling to bolt on similar capabilities. In fact, some legacy database vendors have started retrofitting features like JSON document support or vector search onto their products as afterthoughts, which MongoDB's CEO characterizes as a passive admission that MongoDB's approach is superior. In keeping with its focus on staying at the forefront of modern application development, MongoDB has aggressively embraced the AI wave. A key development was the acquisition of Voyage AI, an AI startup specializing in embedding generation and re-ranking models for search. Announced in early 2025, the Voyage AI deal (approximately a $200+ million purchase) was aimed at redefining the database for the AI era by baking advanced AI capabilities directly into MongoDB's platform. By integrating Voyage's state-of-the-art embedding and re-ranking technology, MongoDB enables its customers to feed more precise and relevant context into AI models, significantly improving the accuracy and trustworthiness of AI-driven applications. In practical terms, this means a company using MongoDB can now do things like generate vectors (embeddings) from its application data, perform semantic searches, and retrieve context for an AI model's queries, all within MongoDB itself. Developers no longer need a separate specialized vector database or search system. MongoDB is already showing progress from this integration. The company released Voyage 3.5, an updated set of AI models, which reportedly outperform other leading embedding models while reducing storage requirements by over 80%. This is a significant improvement in efficiency and accuracy, making AI features more cost-effective at scale for MongoDB users. It also helps solve the AI hallucination problem by grounding LLMs in a trusted database, thereby increasing output accuracy. Beyond Voyage, MongoDB launched broader AI initiatives such as the MongoDB AI Innovators Program (in partnership with major cloud providers and AI firms) to help customers design and deploy AI-powered applications. Early pilot programs using MongoDB's AI features have yielded promising results, dramatically cutting the time and cost needed to modernize legacy applications with AI assistance. MongoDB's Q1 FY2026 delivered strong results above expectations, regaining the company's momentum. Revenue for Q1 came in at $549.0 million, a 22% increase year-over-year (YoY), and comfortably ahead of Wall Street's $528 million consensus estimate. Atlas revenue grew 26% YoY and made up 72% of total revenue in Q1, reflecting strong usage trends. While other segments like Enterprise Advanced and services also posted growth, Atlas remains the primary driver of MongoDB's momentum. The company added approximately 2,600 net new customers in the quarter, bringing the total customer count to over 57,100. This was the highest quarterly addition in six years, suggesting MongoDB's strategy to focus on higher-value clients and strong self-service adoption is paying off. In the words of CEO Dev Ittycheria, we got off to a strong start in fiscal 2026 as MongoDB executed well against its large opportunity. MongoDB has shown significant improvements in profitability and efficiency, although it's still unprofitable on a GAAP basis. The company recorded a non-GAAP operating income of $87.4 million in Q1, which represents a 16% increase. This is a jump from the 7% non-GAAP operating margin a year ago. Operating expenses grew more slowly than planned, particularly due to more measured hiring, which contributed to the margin outperformance. Non-GAAP net income was $86.3 million, or $1.00 per diluted share, doubling Non-GAAP EPS from the prior year period. On a GAAP basis, MongoDB reported a net loss of $37.6 million ($0.46 per share) for the quarter, which is still an improvement from the $80.6 million loss ($1.10 per share) a year earlier. The GAAP loss was much narrower than expected with analysts forecasting a loss of around $0.85 per share. Gross margins remain healthy but are shrinking. Q1 gross margin was 71.2%, 72 basis points lower than last year's 72.8% due to the revenue mix. Despite MongoDB's strong top-line performance, stock-based compensation (SBC) remains elevated, consuming 24% of total revenue in Q1. For a company growing revenue in the high single digits and still unprofitable on a GAAP basis, this level of dilution is concerning. MongoDB's cash flow generation and balance sheet also underscore its improving efficiency. Operating cash flow in Q1 rose to approximately $110 million, up from $64 million a year ago, while free cash flow nearly doubled to $106 million. The improvement was driven by higher operating profits and solid collections, leading MongoDB to end the quarter with $2.5 billion in cash and short-term investments and no debt. In fact, boosted by the quarter's results, MongoDB's Board of Directors authorized an additional $800 million buyback authorization, on top of $200 million authorized last quarter, bringing the total program to $1.0 billion. This is a strong vote of confidence by management in the company's future. It's also a shareholder-friendly move to offset dilution from stock-based compensation and the Voyage deal. Due to a blackout period linked to the CFO transition, no shares were repurchased in Q1, but the company indicated buybacks would begin shortly. To put it in perspective, this share buyback is roughly 5% of MongoDB's market capitalization. Looking ahead, MongoDB management struck an optimistic tone and raised their outlook for the full fiscal year. Citing a strong start to the year, the company increased its FY2026 revenue guidance by $10 million to a range of $2.25$2.29 billion. This implies roughly 13% YoY growth at the midpoint, and it incorporates some conservatism for potential macro headwinds in the second half (including an expected $50 million headwind from lower multi-year license revenue in FY26). Management also boosted its profitability outlook, raising the full-year non-GAAP operating income guidance by 200 basis points in margin. The updated guidance calls for FY26 non-GAAP operating income of $267 to $287 million and non-GAAP EPS of $2.94 to $3.12. Previously, the company had expected $210$230 million in non-GAAP operating income (EPS $2.44$2.62) for the year, so this upward revision is substantial. MongoDB appointed Mike Berry as its new Chief Financial Officer in late May. Berry, a seasoned executive with over 30 years of experience and prior CFO roles at NetApp, McAfee, and FireEye, replaces Michael Gordon, who stepped down earlier this year after nearly a decade with the company. Berry's track record in scaling enterprise software businesses and driving operational discipline aligns well with MongoDB's current phase of improving margins and shareholder returns. MongoDB's stock price has rallied on the back of its strong Q1 report, reflecting renewed investor enthusiasm. Even after this jump, it's still undervalued in some multiples compared to its peers. Source: Author Valuation multiples paint a mixed picture. MongoDB trades at the lowest price-to-sales and price-to-gross-profit ratios among its software peers, which could reflect its strong gross margins. However, it may also signal growing investor skepticism about the company's long-term growth trajectory and ability to convert usage into durable profitability. Meanwhile, MongoDB's price-to-earnings-growth ratio is the highest in the group, primarily because its revenue growth is expected to decelerate into the low double digits. In contrast, peers like Snowflake (NYSE:SNOW) and Datadog (NASDAQ:DDOG) continue to command premium valuations, backed by faster top-line expansion and stronger free cash flow margins. Another way to assess the opportunity is through a discounted cash flow analysis that blends multiple-based and perpetuity growth assumptions. I estimate a fair value of around $210 per share, suggesting the stock is fairly valued after the post-earnings rally. Source: Author Finally, in the first quarter of the calendar year, MongoDB has seen the same number of guru sellers as buyers. Baillie Gifford (Trades, Portfolio), Jefferies Group (Trades, Portfolio), and Paul Tudor Jones (Trades, Portfolio) have reduced their positions in the stock, with some trimming up to 98%. On the other hand, Lee Ainslie (Trades, Portfolio) has created a new position, while Steven Cohen (Trades, Portfolio) and PRIMECAP Management (Trades, Portfolio) add to their position significantly. MongoDB's long-term opportunity remains compelling, but several risks warrant attention. The most immediate is the sensitivity of Atlas revenue to macroeconomic conditions. Because Atlas follows a usage-based pricing model, any slowdown in customer consumption, whether from tighter IT budgets or reduced application traffic, can quickly translate into revenue deceleration. In fact, management acknowledged some softness in April before usage rebounded in May, prompting them to maintain a cautious full-year outlook. A second area of concern lies in the company's non-Atlas license revenue, which is expected to decline at a high single-digit rate this year. This includes a roughly $50 million headwind from multiyear license renewals that took place in the prior year. As customers continue shifting to cloud-based solutions, these traditional license revenues may remain volatile and difficult to predict, creating a drag on MongoDB's overall subscription growth in the short term. Lastly, competition from major cloud providers and open-source alternatives remains persistent. AWS DocumentDB, Google Firestore, and Postgres-based document stores represent credible threats. These platforms are often bundled with broader cloud services or offered at lower price points, creating pricing pressure. MongoDB's advantage lies in its developer-friendly architecture and integrated tooling, but maintaining that lead will require ongoing innovation and execution. MongoDB delivered a strong quarter, regaining some favour with Wall Street following two disappointing earnings periods. The company added its highest number of net new customers in six years, demonstrating continued developer interest and adoption. However, management is forecasting a revenue slowdown, with low double-digit growth expected. SBC also remains elevated, a concern given MongoDB's ongoing GAAP unprofitability. I want to see continued progress on profitability, margin expansion, and more disciplined equity compensation practices, particularly as the company matures. This quarter was a solid step in the right direction, but I need to see more. I'll hold my position for another quarter or two before deciding whether MongoDB can sustainably deliver on its potential. This article first appeared on GuruFocus. 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Yahoo
06-06-2025
- Business
- Yahoo
DA Davidson Reiterates Buy Rating on MongoDB (MDB), Keeps PT
On June 5, DA Davidson analysts reiterated a 'Buy' rating on MongoDB Inc. (NASDAQ:MDB) stock and stuck with the $275 price target. The analysts asserted the rating and price target in response to the company's robust performance in the first quarter. Copyright: limonzest / 123RF Stock Photo MongoDB delivered Atlas and Non-Atlas revenue that topped analysts' expectations, contributing to a 19.2% year-over-year revenue increase. The company achieved non-GAAP earnings per share of $1.00, better than $0.66 expected. Revenue in the quarter totaled $549 million, above the $528.2 million expected. While Atlas consumption remained under pressure in April, it bounced back in May. On the other hand, Atlas's growth accelerated by 26% compared to 24% in the first quarter of last year. Following the better-than-expected first quarter results, MongoDB raised its fiscal year 2026 guidance due to some non-Atlas timing advantages realized in the quarter. It now expects full-year revenue to range between $2.25 billion and $2.29 billion, signaling confidence in continued growth. The company also increased its operating profit projections to affirm improvements in operational efficiency. DA Davidson reaffirmed the Buy rating buoyed by the company's strategic focus on operational efficiency. MongoDB is a company that develops and supports the MongoDB database, a popular NoSQL database known for its flexibility and scalability. It provides a multi-cloud developer data platform, including cloud-based services like MongoDB Atlas, and caters to a wide range of industries, including financial services, telecommunications, and healthcare. While we acknowledge the potential of MDB as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an extremely cheap AI stock that is also a major beneficiary of Trump tariffs and onshoring, see our free report on the best short-term AI stock. READ NEXT: and. Disclosure: None. Sign in to access your portfolio
Malaysian Reserve
05-06-2025
- Business
- Malaysian Reserve
RavenDB Launches GenAI Capabilities Directly into the Operational Data Layer
This new feature will turn the operational database into a powerful native AI engine, responding to the growing demand for engineering teams to deliver GenAI capabilities. HADERA, Israel and SANTA CLARA, Calif., June 4, 2025 /PRNewswire/ — RavenDB, a high-performance NoSQL document database trusted by developers and enterprises worldwide, today announced the launch of its new feature, bringing native Generative AI (GenAI) capabilities directly into its core database engine, marking a new chapter in how data and intelligence converge to drive real-time innovation. RavenDB now brings GenAI to the data layer, eliminating the need for middleware, external orchestration, or costly third-party services. Developers can generate, enrich, classify, and automate content and decisions directly within the database using any large language model (LLM) they choose. By embedding AI where the data lives, RavenDB enables secure, efficient, real-time execution without external add-ons. 'This is more than just a feature, it's a fundamental rethinking of where AI belongs in the software stack,' said Oren Eini, CEO and Founder of RavenDB. 'We're empowering organizations, from startups to global enterprises, to create intelligent applications without complexity or compromise by placing AI where it belongs: inside the data engine.' Unlike most GenAI solutions, which rely on fragile service wrappers or proprietary cloud stacks, RavenDB takes a radically straightforward approach: let your data do more natively. Built-in summarization, classification, and tagging support turn traditional queries into intelligent, real-time actions. The feature also allows RavenDB users to leverage their data to generate additional documents and information, enriching the dataset directly from within the database. In short, your data doesn't just answer questions; it evolves, expands, and works for you. RavenDB's new feature supports any LLM (open-source or commercial), allowing teams to run GenAI tasks directly inside the database. Moving from prototype to production traditionally requires complex data pipelines, vendor-specific APIs, external services, and significant engineering effort. With this feature, RavenDB removes those barriers and bridges the gap between experimentation and production, giving developers complete control over cost, performance, and compliance. The result is a seamless transition from idea to implementation, making the leap to production almost as effortless as prototyping. What sets RavenDB apart is its fully integrated, flexible approach: developers can use any LLM on their terms. It's optimized for cost and performance with smarter caching and fewer API calls, and includes enterprise-ready capabilities such as governance, monitoring, and built-in security, designed to meet the demands of modern, intelligent applications. By collapsing multiple infrastructure layers into a single intelligent operational database, RavenDB's native GenAI capabilities significantly upgrade its data layer. This enhancement accelerates innovation by removing complexity for engineering leaders. Whether classifying documents, summarizing customer interactions, or automating workflows, teams can build powerful features directly from the data they already manage, with no dedicated AI team required. The announcement coincides with the AI and Big Data Expo in Santa Clara, where RavenDB will demonstrate how its new native GenAI feature removes traditional barriers to adoption, marking a strategic evolution in modern application development. Available starting today, the feature includes built-in support for observability, security, and compliance, and is ready for everything from real-time personalization to enterprise-grade document automation. About RavenDB: RavenDB is a hybrid NoSQL document database built for modern application development. Used by 12,000 companies across 50 industries, RavenDB helps teams move faster with seamless data management across cloud, on-prem, and edge environments. With full-text search, automatic indexes, and an easy-to-use studio for monitoring and administration, RavenDB is the database developers love and enterprises trust. Learn more at Media Contact Shahni Ben-Haimshahni@ Logo – View original content:
Yahoo
04-06-2025
- Business
- Yahoo
RavenDB Launches GenAI Capabilities Directly into the Operational Data Layer
This new feature will turn the operational database into a powerful native AI engine, responding to the growing demand for engineering teams to deliver GenAI capabilities. HADERA, Israel and SANTA CLARA, Calif., June 4, 2025 /PRNewswire/ -- RavenDB, a high-performance NoSQL document database trusted by developers and enterprises worldwide, today announced the launch of its new feature, bringing native Generative AI (GenAI) capabilities directly into its core database engine, marking a new chapter in how data and intelligence converge to drive real-time innovation. RavenDB now brings GenAI to the data layer, eliminating the need for middleware, external orchestration, or costly third-party services. Developers can generate, enrich, classify, and automate content and decisions directly within the database using any large language model (LLM) they choose. By embedding AI where the data lives, RavenDB enables secure, efficient, real-time execution without external add-ons. "This is more than just a feature, it's a fundamental rethinking of where AI belongs in the software stack," said Oren Eini, CEO and Founder of RavenDB. "We're empowering organizations, from startups to global enterprises, to create intelligent applications without complexity or compromise by placing AI where it belongs: inside the data engine." Unlike most GenAI solutions, which rely on fragile service wrappers or proprietary cloud stacks, RavenDB takes a radically straightforward approach: let your data do more natively. Built-in summarization, classification, and tagging support turn traditional queries into intelligent, real-time actions. The feature also allows RavenDB users to leverage their data to generate additional documents and information, enriching the dataset directly from within the database. In short, your data doesn't just answer questions; it evolves, expands, and works for you. RavenDB's new feature supports any LLM (open-source or commercial), allowing teams to run GenAI tasks directly inside the database. Moving from prototype to production traditionally requires complex data pipelines, vendor-specific APIs, external services, and significant engineering effort. With this feature, RavenDB removes those barriers and bridges the gap between experimentation and production, giving developers complete control over cost, performance, and compliance. The result is a seamless transition from idea to implementation, making the leap to production almost as effortless as prototyping. What sets RavenDB apart is its fully integrated, flexible approach: developers can use any LLM on their terms. It's optimized for cost and performance with smarter caching and fewer API calls, and includes enterprise-ready capabilities such as governance, monitoring, and built-in security, designed to meet the demands of modern, intelligent applications. By collapsing multiple infrastructure layers into a single intelligent operational database, RavenDB's native GenAI capabilities significantly upgrade its data layer. This enhancement accelerates innovation by removing complexity for engineering leaders. Whether classifying documents, summarizing customer interactions, or automating workflows, teams can build powerful features directly from the data they already manage, with no dedicated AI team required. The announcement coincides with the AI and Big Data Expo in Santa Clara, where RavenDB will demonstrate how its new native GenAI feature removes traditional barriers to adoption, marking a strategic evolution in modern application development. Available starting today, the feature includes built-in support for observability, security, and compliance, and is ready for everything from real-time personalization to enterprise-grade document automation. About RavenDB: RavenDB is a hybrid NoSQL document database built for modern application development. Used by 12,000 companies across 50 industries, RavenDB helps teams move faster with seamless data management across cloud, on-prem, and edge environments. With full-text search, automatic indexes, and an easy-to-use studio for monitoring and administration, RavenDB is the database developers love and enterprises trust. Learn more at Media Contact Shahni Ben-Haimshahni@ Logo - View original content to download multimedia: SOURCE RavenDB
International Business Times
28-05-2025
- Business
- International Business Times
Revolutionizing Media & Entertainment: The Industry Leading Approach By Raghavendra Sridhar
Unlock the Future of Media with AI, Multi-Cloud, and Big Data-Powered by Expertise Step into the next era of media and entertainment, where innovation meets impact. With 20 years of pioneering expertise, Raghavendra Sridhar is at the forefront of transforming the industry, harnessing the power of artificial intelligence, multi-cloud solutions, and big data to drive growth, engagement, and operational excellence. AI-Driven Personalization: Captivate Every Audience AI-powered strategies deliver hyper-personalized content recommendations and marketing campaigns that keep viewers engaged and coming back for more. By analyzing user behavior and preferences, his solutions enable streaming platforms and publishers to: Serve up tailored content and ads, boosting viewer satisfaction and loyalty Increase subscription renewals and drive millions in additional revenue Automate content production, reducing time-to-market and cutting costs Just like industry leaders Netflix and Spotify, AI systems predict trends and personalize the user journey, ensuring brands stay always ahead of the curve. These solutions not only elevate the viewing experience but also empower content creators to experiment with new formats and storytelling techniques, confident that data-driven insights will guide their creative decisions. Multi-Cloud Mastery: Scale Without Limits Break free from single-cloud constraints. multi-cloud architectures empower media companies to: Achieve global scalability and reliability by leveraging AWS, Google Cloud, Azure, and more Optimize costs and avoid vendor lock-in Enhance disaster recovery and streamline operations for seamless content delivery This flexibility ensures businesses can expand its reach, reduce overhead, and deliver services more efficiently-fueling both growth and profitability. With multi cloud, companies can rapidly deploy new features, adapt to changing viewer demands, and ensure uninterrupted access to content, regardless of audience location. Big Data Brilliance: Turn Insights into Revenue In today's data-driven world, industry experts like Raghavendra unlock the full potential of big data technologies like Hadoop, Spark, and NoSQL to: Micro-segment audiences for precision ad targeting and increased ROI Analyze vast viewer datasets to inform content creation and marketing strategies Identify new market opportunities and boost advertising revenue With actionable insights available, organizations can make smarter decisions, create more engaging content, and maximize every revenue stream. Big data analytics also enable real-time performance monitoring, allowing rapid optimization and continuous improvement. Continuous Innovation: Stay Ahead, Always A relentless pursuit of innovation ensures businesses are equipped with the latest tools and methodologies. Emerging technologies are integrated seamlessly to keep operations agile and offerings cutting edge. "Innovation, Executed with Expertise"- this is the guiding principle. Raghavendra is constantly evaluating advancements in AI, cloud, and analytics, ensuring his clients benefit from early adoption and sustained competitive advantage. Whether it's implementing next-gen recommendation engines or integrating AI-powered automation into production workflows, a forward-thinking approach keeps businesses future ready. Why Choose This Approach? Proven Impact: Millions in additional revenue generated for leading media brands Millions in additional revenue generated for leading media brands End-to-End Solutions: From data strategy to AI implementation and cloud optimization From data strategy to AI implementation and cloud optimization Future-Ready: Solutions designed to evolve with industry trends and consumer behaviors A consultative approach ensures that every solution is tailored to unique business needs, fostering long term partnerships and delivering measurable results. Shape the Future of Media & Entertainment Don't just keep up lead the transformation. With expertise in AI, multi cloud, and big data, businesses can: Enhance customer experiences Optimize workflows Accelerate growth and profitability Experience the power of innovation. Elevate the media business with cutting-edge technology solutions. "Results Through Revolutionary Tech. Elevating Industry Standards." Let Raghavendra Sridhar help unlock new levels of success in the media and entertainment industry where data-driven innovation meets creative excellence.
