Founded by Hari Subramaniam, LifeSigns has developed a predictive patient monitoring platform designed to address one of healthcare’s most persistent gaps: the inability of traditional periodic observation systems to detect rapidly changing patient risks in real time. The platform has already been deployed across more than 50 hospitals in India, particularly in tier 2 cities where healthcare infrastructure and staffing challenges remain significant.

The company’s US-FDA approved platform combines a medical-grade wearable device, a cloud-based monitoring dashboard, and a predictive AI engine capable of continuously tracking key patient vitals. These include ECG, heart rate, respiration, temperature, blood pressure, and oxygen saturation. By integrating real-time patient data into an AI-led analytics system, the platform can identify early signs of patient deterioration up to 26 hours in advance and flag critical risks within a four-hour window.

The technology is designed to function across the full healthcare continuum, including ambulances, emergency rooms, intensive care units, general wards, and home-care environments. In addition, a central monitoring command centre enables hospital staff to remotely oversee multiple patients simultaneously, helping healthcare providers address the ongoing shortage of nursing and clinical personnel, particularly in non-metro regions.

According to the company, the platform has generated more than 87,000 life-saving alerts to date, reduced code blue incidents by nearly 90%, and lowered ICU readmissions by close to 78%. These outcomes reflect the growing role of AI, predictive analytics, and digital transformation in reshaping healthcare delivery and improving patient safety.

LifeSigns also operates on a frugal, occupancy-based pricing model, allowing hospitals to pay based on patient volumes rather than fixed subscription costs. This approach is aimed at making advanced patient monitoring technology more accessible to smaller hospitals and healthcare facilities across emerging markets and underserved regions.

The investment comes amid a broader surge in demand for AI-enabled healthcare infrastructure, as providers increasingly prioritise data-driven decision-making, operational scalability, and preventive care models. With predictive monitoring gaining traction globally, healthtech startups leveraging AI and cloud-based systems are expected to play a larger role in the future of connected healthcare.

“No patient should deteriorate silently because systems are not designed to continuously monitor them. That belief built LifeSigns. Our platform has demonstrated both clinical impact and scalability in India. With HealthQuad and Quadria, we now have the strategic depth to take this model global and make predictive care the standard,” said Hari Subramaniam, Founder and CEO of LifeSigns.

Rahul Agarwal, Partner at HealthQuad, said, “Healthcare today is built on periodic observation while risk is real time. The future lies in continuous monitoring led predictive intelligence. LifeSigns is building that layer to detect deterioration early and enable timely intervention which can significantly improve outcomes and reduce healthcare costs at scale. This is a globally relevant model and where the next wave of healthcare will be built. With access to Quadria’s ecosystem, we are well positioned to help Hari and LifeSigns team to scale across global markets.”

In a highly competitive AI market, Sierra has actively highlighted its rapid growth trajectory. Initially, the company started with just four design partners a few years ago. However, it now claims that more than 40 percent of the Fortune 50 companies use its platform. Additionally, AI agents running on Sierra’s platform currently handle billions of interactions, including mortgage refinancing, insurance claims processing, product returns, and nonprofit fundraising campaigns.

Furthermore, the AI startup has demonstrated aggressive revenue expansion. The company reported reaching $100 million in annual recurring revenue (ARR) in late November, and shortly afterward, it announced that it had scaled to $150 million in ARR by early February. This rapid growth reflects both increasing enterprise demand for AI solutions and the significant investments required to implement them.

Bret Taylor, who also serves as chairman of OpenAI and previously worked as co-CEO of Salesforce, has emphasized that agentic AI can ultimately reduce costs and increase revenue for businesses. However, he has also acknowledged that companies often face high upfront costs during the initial deployment phase.

This trend recently surfaced during a discussion at one of the events. During the conversation, Praveen Neppalli Naga, CTO of Uber, explained how the company rapidly consumed its AI budget after adopting agentic AI tools late last year. At the same time, he noted that Uber has started to observe meaningful results from these investments.

Notably, Uber has already integrated AI deeply into its engineering workflows. Approximately 10 percent of all code produced across its 8,000 engineers and technical staff is now generated autonomously. He added that “10% at our scale is huge.” In a practical demonstration, Uber assigned one team to build a new hotel-booking integration entirely using agentic workflows. Consequently, the team completed a project that would typically take a year in just six months.

Meanwhile, Sierra continues to expand its product capabilities beyond customer-facing AI agents. In April, the company launched Ghostwriter, an “agent as a service” tool that enables users to build other AI agents. With this feature, users can describe their requirements in natural language, and Ghostwriter autonomously creates and deploys specialized agents to perform those tasks.

For Bret Taylor, this innovation supports a broader vision he shared at the HumanX conference in San Francisco. He argued that many enterprise software tools remain underutilized, as employees interact with platforms like Workday only during specific events such as onboarding or open enrollment. Therefore, the AI startup and its investors envision a future where users no longer need to navigate complex enterprise systems, as AI agents will handle tasks seamlessly in the background.

With this investment, NudgeBee will accelerate the development of its enterprise context layer while simultaneously scaling its partnership-led distribution model. Moreover, the company will invest in customer success and deployment capabilities so that enterprises can realise value faster from their cloud infrastructure investments.

Founded in 2024 by Rakesh Rajendran and Shiv Pratap Singh, NudgeBee focuses on solving a critical inefficiency in modern computing environments. As organisations increasingly adopt cloud-native and multicloud architectures, their monitoring capabilities have improved significantly. However, execution—the ability to resolve issues effectively—has not advanced at the same pace.

Consequently, engineering teams often struggle with fragmented tools and an overwhelming volume of alerts, which leads to delayed responses and rising operational costs. To address this challenge, NudgeBee leverages a semantic knowledge graph to map complex relationships across multiple data points within cloud systems.

Furthermore, by integrating telemetry data—automatically collected from distributed systems—with infrastructure topology, NudgeBee builds a unified operational foundation. This approach enables the platform to analyse historical patterns while understanding the real-time state of systems, thereby improving decision-making and execution.

The co-founders emphasised that while modern teams have access to numerous dashboards, they still lack connected context and reliable execution mechanisms. They highlighted that operational inefficiencies persist because critical knowledge remains fragmented across tools and teams, ultimately slowing down engineering workflows.

By consolidating these elements, NudgeBee empowers AI agents to not only detect system issues but also take actionable steps within existing workflows. In addition, the platform offers specialised tools such as AI-SRE agents, which apply Site Reliability Engineering principles to maintain system stability, and an AI FinOps assistant that optimises cloud spending by identifying and executing cost-saving opportunities.

“NudgeBee stands out in its ability to connect signals across the stack and translate them into reliable action while integrating seamlessly with existing engineering workflows. With strong early traction, the team is building a platform that reflects how modern cloud teams actually operate,” said Sampath P, Partner, Kalaari Capital.

NudgeBee’s latest funding round highlights the growing demand for AI-powered cloud operations platforms that go beyond monitoring to enable intelligent execution. As enterprises continue to scale their cloud infrastructure, solutions like NudgeBee are poised to play a crucial role in improving efficiency, reducing costs, and enhancing system reliability.

The firm plans to use the new fund to expand its investments in large, high-growth companies, particularly in the artificial intelligence space. Notably, Sequoia has already backed major AI players such as OpenAI and Anthropic, whose demand for high-performance computing infrastructure has fueled unprecedented capital requirements. As a result, this expansion fund positions Sequoia to deepen its exposure to the AI boom.

Importantly, the $7 billion vehicle will support Sequoia’s expansion strategy, which focuses on late-stage investments across the US and Europe. This fund significantly surpasses the firm’s previous $3.4 billion expansion fund raised in 2022, highlighting increased investor confidence and a more aggressive capital deployment approach. The fundraising took place earlier this year, according to sources who requested anonymity due to the private nature of the discussions.

Sequoia declined to comment on the development.

Meanwhile, this fundraising marks a leadership transition phase for the firm. Last year, Roelof Botha handed over leadership responsibilities to Alfred Lin and Pat Grady, who now serve as co-stewards. Under their direction, Sequoia has actively restructured its investment strategy and leadership roles to align with evolving market dynamics.

In addition, the firm has made several key organizational changes. In March, Sequoia appointed former senior steward Doug Leone as chairman, thereby bringing him back into a more active investing role. Furthermore, the firm strengthened its investment team by adding Liam Corrigan and Sonali Singh. It also rehired Carl Eschenbach, who previously left in 2022 to serve as co-CEO of Workday Inc.

At the same time, some investors have stepped back from their roles. Josephine Chen, Charlie Curnin, and Cornelius Menke have exited their positions, while Ravi Gupta has scaled back his involvement to launch a new startup, though he continues to remain a partner.

Crucially, Sequoia continues to place significant bets on leading AI companies, even as competition intensifies within the sector. Its portfolio includes Anthropic, OpenAI, and Elon Musk’s xAI, now part of SpaceX. These companies are actively preparing for potential public listings in 2026, which could generate substantial returns for the firm if market conditions remain favorable.

However, Sequoia has not limited its focus solely to AI-native businesses. The firm also continues to invest in mature companies across sectors. For instance, one of its most notable recent exits involved Wiz, a cybersecurity company that Alphabet acquired for $32 billion in a deal that closed last month. This demonstrates Sequoia’s diversified investment approach beyond AI.

Moreover, this $7 billion fund builds on the firm’s previous $2.5 billion fundraising announced last year, which targeted seed, venture, and growth-stage investments. As of the end of last year, Sequoia Capital managed over $80 billion in assets, according to regulatory filings, reinforcing its status as one of the largest venture capital firms globally.

Sequoia Capital’s $7 billion expansion fund underscores its aggressive growth strategy and long-term conviction in artificial intelligence, late-stage startups, and global innovation ecosystems. By doubling down on high-potential companies and strengthening its leadership structure, the firm aims to maintain its dominance in venture capital while capturing the next wave of technological transformation.

Although the companies did not disclose the financial terms of the deal, Hiro Finance also never revealed its total funding. However, since the startup confirmed it will shut down operations on April 20 and permanently delete all user data from its servers by May 13, industry observers widely view the transaction as an acquihire.

Meanwhile, Ethan Bloch stated that Hiro Finance employees will join OpenAI as part of the transition. While he did not confirm the exact number, LinkedIn data suggests that the startup had around 10 employees. He did not respond to additional requests for comment.

Founded in 2023, Hiro Finance launched its AI-powered financial planning tool just five months ago. The platform enabled users to input key financial details such as salary, debts, and monthly expenses. Subsequently, the app generated multiple “what-if” scenarios, helping users make more informed financial decisions.

Moreover, the product was specifically trained to deliver accurate financial calculations. It even included a feature that allowed users to verify the accuracy of outputs, as Bloch demonstrated earlier. Over time, advanced AI models have significantly improved their mathematical capabilities; however, historically, achieving consistent accuracy in financial computations remained a challenge.

Notably, this deal carries additional significance because Ethan Bloch previously founded Digit, a digital banking platform that automated savings for users. Later, Digit was acquired by Oportun in 2021 for more than $200 million.

Furthermore, this is not the first time OpenAI has acquired a financial-focused application. Since the company already positions ChatGPT as a valuable tool for business and finance teams, the acquisition aligns with its broader strategy to strengthen capabilities in financial services.

At the same time, it remains unclear whether OpenAI will launch a dedicated financial planning product or simply integrate Hiro’s expertise into its existing ecosystem. Additionally, industry observers suggest that the move could help OpenAI appeal to users of OpenClaw, who often prefer Claude. OpenClaw widely powers automated stock trading, and notably, Bloch also developed his own auto-trading agent called RoboBuffett, which he shared on LinkedIn.

OpenAI’s acquisition of Hiro Finance highlights its continued push into financial AI and talent acquisition. While the immediate impact appears to center on strengthening internal expertise, the move could eventually translate into more advanced financial tools for users. As AI adoption accelerates across industries, this deal further reinforces OpenAI’s intent to expand its influence in high-value, data-driven domains like personal finance.

At the same time, the company introduced its Real-Time Analytics Module, a patented technology that integrates supply chain and marketing analytics directly with core business operations.

Founded by Priyanka Aeron, Thrive Global AI aims to solve a persistent challenge faced by brands operating across multiple markets—fragmented data spread across regions. This fragmentation often leads to inconsistent insights, delayed reporting, and slower business decision-making.

To address this issue, the platform consolidates data from multiple business functions. Moreover, it frequently processes information within a client’s own cloud infrastructure, ensuring both data security and regulatory compliance. The system also connects seamlessly with internal enterprise systems and third-party platforms such as Amazon and Noon through API integrations.

Unlike many generic AI tools available today, Thrive Global AI develops custom large language models (LLMs) tailored to each client’s supply chain cycles, business benchmarks, and operational definitions. According to the company, these customised models are typically deployed within two to three weeks, enabling faster implementation and measurable results.

Furthermore, the platform merges offline and online sales data to create what the company describes as a single source of truth. As a result, it effectively functions as a company-specific AI assistant that supports data-driven business decision-making.

The technology supports several practical use cases, including predicting stock-out risks, identifying high-return investment opportunities, improving refill planning, and optimising marketing expenditure. According to the company, brands using the platform have reported 200–300 percent growth, driven by improved capital efficiency, stronger inventory control, and better marketing timing.

Commenting on the company’s vision, founder Priyanka Aeron emphasized that the future of artificial intelligence will rely less on experimentation and more on effective integration into real-world business operations.

“AI’s future will not be shaped by who experiments the fastest but by who integrates it the smartest. At Thrive Global AI, we are embedding intelligence directly into supply chains, capital strategy, and marketing to deliver measurable revenue outcomes,” she said.

Currently, around 80 percent of Thrive Global AI’s revenue comes from platform subscriptions, access fees, and brand management services, reflecting a strong recurring revenue model.

Looking ahead to 2026, the company plans to focus on enterprise-scale adoption, governance-driven innovation, and sector-specific AI solutions. In particular, it aims to target industries that operate under strict regulatory and compliance frameworks.

Additionally, Thrive Global AI plans to further invest in data governance frameworks and scalable decision-intelligence systems to enhance operational efficiency and reduce costly trial-and-error approaches in enterprise AI adoption.

The companies unveiled the collaboration at the India AI Impact Summit 2026 in New Delhi. Notably, the partnership combines Nvidia’s global startup ecosystem with Activate’s early-stage investing model that supports founders even before a company formally exists.

Under the collaboration, Nvidia will integrate its global NVIDIA Inception program with Activate’s “pre-company” venture approach. As a result, the partnership will focus on idea-to-product incubation, where founders receive support from day zero rather than after incorporation.

Aakrit Vaish, Founder of Activate, explained that the firm designs its model specifically for deeply technical founders at the idea stage. Moreover, he stated that Activate involves partners and advisors early to co-create companies from inception. Consequently, the collaboration extends this philosophy through Nvidia’s global AI startup ecosystem.

Through the tie-up, Activate-backed startups will gain direct access to Nvidia’s developer stack. Specifically, founders will receive technical training, compute resources, reference workflows, and deployment support. Additionally, startups will leverage the Nemotron family of open AI models and Nvidia NIM microservices to accelerate product development and scaling.

Tobias Halloran, Director of EMEAI Startups and Venture Capital at Nvidia, emphasized that Nvidia accelerates founder momentum by offering access to accelerated computing and scalable AI infrastructure. Furthermore, he highlighted programs such as Nvidia Inception and the Nvidia VC Alliance, which help startups build for global markets from India.

The partnership builds on Nvidia’s expanding engagement with India’s AI ecosystem. Previously, Nvidia has worked closely with venture capital firms such as Peak XV, Accel, Nexus Venture Partners, and Elevation Capital to identify and support emerging AI startups.

As a result, these efforts signal a strategic shift for Nvidia. Rather than acting solely as a hardware supplier, Nvidia now plays an active role in shaping India’s AI startup pipeline by connecting capital, infrastructure, and developer enablement.

At the same time, the collaboration reflects a broader shift in investor behavior across India’s AI ecosystem. Currently, AI funding concentrates heavily at the earliest stages, where founders test use cases and build technical depth before scaling revenue.

Activate has positioned itself as a pre-company venture platform that works with founders from inception. According to Vaish, investors now prioritize proof of progress, such as working prototypes and customer validation, over near-term monetization.

By aligning Activate’s founder-first model with Nvidia’s advanced tooling and compute infrastructure, the partnership aims to close a critical gap in India’s AI journey. Specifically, the collaboration pairs strong technical talent with access to high-performance computing and global deployment pathways.

Going forward, both companies will jointly identify high-potential founders across India. Additionally, they will run developer enablement and community programs to help teams prototype, optimize, and scale AI applications on Nvidia GPUs.

The move comes as global technology providers and domestic investors increasingly view India as a key hub for applied AI innovation. Importantly, much of the value creation now occurs at the application layer rather than in building large foundational models.

For Nvidia, the collaboration reinforces its long-term strategy to seed demand for accelerated computing. As India ramps up public and private investment under the IndiaAI Mission, Nvidia aims to ensure that the next generation of AI startups builds natively on its platform.

Through this initiative, Qualcomm Ventures will deploy capital across startups at various stages. Notably, the fund will focus sharply on AI-led innovation spanning automotive technologies, the Internet of Things (IoT), robotics, and mobile solutions.

Commenting on the launch, Qualcomm Incorporated President and CEO Cristiano Amon said on February 18, “Through our new Strategic AI Venture Fund, Qualcomm is investing in companies that are advancing the next chapter of AI in India.”

Furthermore, the move strengthens Qualcomm’s long-standing strategic bet on India and aligns seamlessly with its global push to advance edge AI, where intelligence operates directly within devices and systems rather than relying solely on cloud infrastructure.

Expanding on this vision, Amon added in a statement, “AI is entering a new phase where intelligence is built directly into the devices and systems people rely on every day—from smartphones and PCs to cars, industrial machines, and robots. This shift will reshape entire industries, and India’s startup ecosystem has a critical role to play as edge AI drives innovation across sectors.” As India continues to emerge as a dynamic innovation hub, Qualcomm sees a strong opportunity to help startups develop scalable, market-leading AI solutions.

In parallel, Qualcomm is positioning itself as a key enabler of efficient, secure, and high-performance edge AI by actively backing founders who build technologies with real-world industrial impact.

Reaffirming this commitment, Quinn Li, Senior Vice President at Qualcomm Technologies and Global Head of Qualcomm Ventures, said, “India has become one of the world’s most vibrant centres of innovation, and we’re proud to deepen our long-standing commitment to its startup ecosystem.”

He further added, “This additional investment expands our support for founders building the next generation of transformative technologies.”

Notably, Qualcomm Ventures, the investment arm of Qualcomm Incorporated, has invested globally since 2000. In India, Qualcomm has backed more than 40 startups since 2007, offering not only growth capital but also access to its deep technology expertise and global ecosystem.

Over the years, Qualcomm’s India portfolio—past and present—has included major names such as Reliance Jio, MapMyIndia, ideaForge, Shadowfax, Cavli Wireless, SpotDraft, and Tonetag.

Qualcomm’s $150 million Strategic AI Venture Fund marks a significant boost for India’s AI and deeptech startup ecosystem. By focusing on edge AI and real-world applications across industries, Qualcomm is not only strengthening its presence in India but also enabling the next wave of globally competitive AI innovation built from the country.

Meanwhile, sources said that General Catalyst is holding talks to lead the new funding round. At the same time, insiders revealed that Modal’s annualized revenue run rate (ARR) stands at approximately $50 million. However, the discussions remain at an early stage, and the final terms could still evolve.

In response, Modal Labs co-founder and CEO Erik Bernhardsson denied that the company is actively raising capital and described his recent conversations with venture capitalists as general discussions rather than formal fundraising efforts. Meanwhile, General Catalyst declined to comment on the matter.

Notably, Modal focuses on optimizing AI inference, which refers to the process of running trained artificial intelligence models to produce outputs in response to user prompts. By improving inference efficiency, companies can significantly lower compute costs while also reducing latency between a user’s request and the model’s response.

Against this backdrop, Modal stands among a small but rapidly growing group of inference-focused startups drawing intense investor interest. Just last week, competitor Baseten announced a $300 million funding round at a $5 billion valuation, more than doubling its $2.1 billion valuation from September. Similarly, Fireworks AI, an inference cloud provider, raised $250 million at a $4 billion valuation in October.

Additionally, in January, the creators of the open-source inference project vLLM transitioned the tool into a VC-backed company called Inferact. The new startup raised $150 million in seed funding, led by Andreessen Horowitz, at an $800 million valuation. At the same time, reports said that the team behind SGLang commercialized its technology as RadixArk and secured seed funding at a $400 million valuation led by Accel, according to sources.

Modal Labs traces its origins to 2021, when Erik Bernhardsson founded the company after spending more than 15 years building and leading data teams. Previously, he held senior roles at companies such as Spotify and Better.com, where he served as CTO.

Earlier in its journey, Modal attracted backing from prominent investors, including Lux Capital and Redpoint Ventures, further cementing its position in the rapidly expanding AI inference infrastructure ecosystem.

Under this three-year commitment, Perplexity will deploy its AI models through Microsoft’s Foundry service, including models built by OpenAI, Anthropic, and xAI, according to people familiar with the agreement who requested anonymity because the matter remains private.

Commenting on the development, a Perplexity spokesperson said, “We are excited to partner with Microsoft for access to frontier models from X, OpenAI, and Anthropic.”

At the same time, the spokesperson clarified that the Microsoft partnership does not reduce Perplexity’s spending on Amazon Web Services (AWS), which has long served as the startup’s primary cloud provider.

Emphasising this continuity, the spokesperson added, “AWS remains Perplexity’s preferred cloud infrastructure provider, and we’re excited to announce expansions of that partnership in the coming weeks.”

Meanwhile, both Microsoft and Amazon declined to comment on the deal.

Although Perplexity ranks among the more richly valued AI startups, it continues to face intense competition from Alphabet Inc.’s Google and OpenAI in its effort to redefine how people search for information online. Additionally, Perplexity has raised significantly less capital than OpenAI and Anthropic, both of which have recently pursued a wave of large-scale infrastructure partnerships.

In general, large enterprises rely on multiple cloud providers to access specialised services while reducing dependence on any single vendor. Notably, this multi-cloud strategy has accelerated in the AI era as companies experiment with emerging tools and sign agreements with both model developers and the cloud platforms that support them.

Historically, the AI startup built much of its business on AWS, using Amazon’s Bedrock service to access Anthropic models for its AI-powered search engine. Perplexity Chief Executive Officer Aravind Srinivas has frequently spoken at AWS conferences and stated during a 2023 appearance that he chose to go “all-in” on Amazon’s cloud.” In turn, AWS has regularly showcased Perplexity as a leading-edge AI customer using its services.

However, in recent months, the relationship between the two companies has grown contentious. In November, Amazon sued Perplexity, seeking to prevent the startup from enabling consumers to use its AI tools to shop and purchase products from Amazon’s online marketplace. In response, Perplexity described Amazon as a bully and labeled the lawsuit “a threat to user choice.” Srinivas also said in November that Perplexity has made “hundreds of millions” of dollars in commitments to AWS.

For Microsoft, the Perplexity agreement strengthens its strategy to position Azure as a premier platform for building AI applications and deploying models from multiple vendors. While Microsoft has long offered models from its partner OpenAI, it further expanded its ecosystem by signing a deal with Anthropic in November.

Highlighting this approach, Microsoft Chief Executive Officer Satya Nadella said during an earnings call earlier this week, “Our customers expect to use multiple models as part of any workload,” adding, “And we offer the broadest selection of models of any hyperscaler.”

Nadella further noted that more than 1,500 Microsoft Foundry customers have already used both OpenAI and Anthropic models. Moreover, he said the number of customers spending over $1 million per quarter on Foundry rose nearly 80% in the quarter that ended in December.