Build fast. Scale smart. Sell into the enterprise.

Don’t build another point solution that sits outside the clinical workflow. Build where healthcare already works. Integrate with Dragon Copilot to bring your differentiated capabilities directly into documentation, patient intake, care coordination, prior authorization, revenue cycle, and clinical decision support, without giving clinicians one more disconnected tool to learn. Use Azure Health Data Services as the standards-based backbone for FHIR, DICOM, and MedTech data, so your product can connect to EHRs and clinical systems while meeting enterprise expectations for interoperability, governance, and scale. Then use Azure AI Foundry to choose and evaluate models, build and orchestrate agents, ground experiences in healthcare data, and add the responsible AI controls and monitoring customers expect. The result: spend less time rebuilding infrastructure Microsoft already provides and more time creating the breakthrough experience only your startup can deliver, with a stronger story for buyers and a faster path from pilot to enterprise rollout.

Health and life sciences innovation doesn’t stop at one point in the value chain, and neither should your ambition. Start with Microsoft Discovery to build specialized AI agents that can reason across scientific literature and proprietary research data, helping teams move faster from target identification to promising hypotheses. Use Azure AI Foundry to select and evaluate models, orchestrate agents, ground experiences in trusted data, and bring responsible AI controls into development from day one. As therapies and devices move into clinical development, Azure Health Data Services helps connect FHIR, DICOM, and MedTech data across research, trial, and care environments. Healthcare-specific models such as MedImageParse, MedImageInsight, and CXRReportGen can accelerate imaging workflows, while Dragon Copilot creates opportunities to bring differentiated startup capabilities directly into clinicians’ daily work. From discovery to trials to the point of care, Microsoft gives startups the building blocks to turn breakthrough ideas into scalable solutions, and ultimately help customers improve patient outcomes.

Health and life sciences startups are advancing population health, patient care, healthcare operations, and clinical research with enterprise deployments. CueZen works with Singapore’s Health Promotion Board to deliver AI-powered, personalized health interventions at national scale. Its NudgeRank system, evaluated in a 12-week Singapore study, increased daily steps by 6.17% and weekly exercise minutes by 7.61% among recipients of personalized nudges. Humata Health works with large health systems (including three of the country’s five largest) to streamline prior authorizations with AI and automation. Its platform automates the process from order through submission and status monitoring, and Humata reports 80% higher clinical-bundling efficiency, 60% fewer peer-to-peer reviews, and a 96% first-pass approval rate. Ignite Data landed Mayo Clinic with an enterprise agreement for multi-site oncology trial automation, plus Memorial Sloan Kettering and AstraZeneca collaborations that are revolutionizing how clinical data flows from EHR to research. Raapid, backed by M12 (Microsoft Ventures) and UPMC Enterprises, earned Microsoft Healthcare AI Certified Software status and reports 5× ROI with $2,000–$4,000 additional revenue per member across health plan customers. Ema Unlimited is embedding AI employees into Fortune 1000 healthcare workflows for patient intake and administrative automation, claiming 70%+ workflow automation from day one. These are production deployments at the world's most recognized medical institutions and healthcare organizations, proving that AI built with enterprise-grade compliance can pass the healthcare industry's most rigorous security and privacy standards.

Innovation doesn't create value while it's being evaluated. For a startup, that's growth deferred. For an enterprise, it's a problem left unsolved and a platform running below its potential. Neither side is short of ambition. Both are short of time.

Build on Azure and your solution shows up enterprise-ready, on infrastructure they already own and already trust. Adoption gets easier, so your technology reaches enterprises faster. And because it extends what they've already invested in rather than sitting beside it, your innovation doesn't just solve a new problem, it raises the return on an investment they've already made.

Every major shift in computing has produced a security category that didn't exist before it. Networking gave us the firewall. The cloud gave us posture management. AI is doing it again, except this time the category is still being defined. Organizations are adopting AI technology faster than the tools to govern it can arrive, and that gap is where the next generation of security companies will be built.

Proximity matters. On Azure the identity, endpoint, and data controls an enterprise enforces with are the same estate you build in, and confidential computing means the telemetry you need stays encrypted even while you're training on it. That's what separates a product that reports a problem from one that resolves it. The companies that manage it don't join a category. They define it.

Cybersecurity startups are protecting the world's most critical infrastructure with deployments that span Fortune 500 enterprises, financial institutions, and government agencies. Silverfort secures 1,000+ enterprise customers including Airbus, Ryanair, Rio Tinto, and UPS, analyzing 10 billion authentications daily with 94% gross retention. Abnormal Security crosses $100M in ARR in 4 years, protecting over 12% of the Fortune 500, including Xerox, Maersk, and Mattel. Cyera grew 353% year-over-year among Fortune 500 customers, landing AT&T, Paramount, Nordstrom, Peloton, Chipotle, and DocuSign on its way to $100M+ ARR and a $6B valuation. Endor Labs convinced Atlassian to replace its incumbent vendor, then signed OpenAI, Robinhood, Cursor and Dropbox; Robinhood reported cutting security findings sent to developers by 95%. RegScale saved the U.S. Navy's NIWC Pacific 36+ weeks on compliance automation. Blink Ops grew revenue 600%+ in 2023, moving multiple Fortune 500 customers from pilot to production.

In financial services, integration risk IS business risk. Banks risk downtime on payment rails, risk introducing latency in fraud detection, and risk breaking audit trails to adopt a new vendor. That’s why one of the first things financial institutions evaluate startups for is how their solutions integrate. The questions are immediate and specific: Does it support our core banking platform? Can we maintain transaction throughput? Will it preserve compliance logging? Startups built on Azure can integrate with core banking platforms like Temenos, FIS, and Jack Henry, payment networks including SWIFT, ACH, and card networks, and fraud detection systems from day one. Support for encryption, transaction monitoring, API-driven integrations, and comprehensive audit logging allows financial institutions to deploy solutions within existing workflows while meeting regulatory expectations. Solutions fit into payment rails and settlement systems while aligning with performance, audit, and infrastructure expectations.

Rule-based systems process transactions. AI-powered systems understand them. Financial institutions are moving from static rules to adaptive intelligence models that detect fraud by behavior, assess credit risk from alternative signals, and personalize financial guidance based on individual patterns. But building AI at financial scale requires infrastructure that can train on billions of transactions and deploy models with sub-second latency. Azure's AI infrastructure enables financial startups to build intelligence across the product stack. Fraud detection models can help identify synthetic identity schemes during account opening and can help flag account takeover before funds transfer. Credit models score thin-file applicants in real time, supporting real-time lending decisions. Personalization engines surface spending insights and recommend products based on transaction history. Document processing models automate KYC verification, helping streamline multi-day processes. These capabilities run on the same infrastructure, trained on transaction-scale datasets, deployed with the low latency financial products demand, helping detection stay ahead of threats instead of cataloging them after the damage is done.

Financial institutions are under pressure to modernize while meeting some of the world's toughest regulatory and security standards. Startups built with Microsoft are rising to meet both demands across global banks, fintech disruptors and professional services firms. Laurel landed Ernst & Young, Grant Thornton, and Crowell & Moring among 100+ top legal, accounting, and consulting firms, growing 300% in ARR and 500% in usage in 12 months before closing a $100M Series C. Endor Labs signed Robinhood, which reported cutting security findings sent to developers by 95%. Wonderful has deployed production AI agents at Banco Caja Social and Bank Hapoalim. At Banco Caja Social, its collections agent increased contact-to-promise conversion from 45% to 65%. Ema Unlimited is automating workflows at Fortune 1000 fintech companies including TrueLayer and Moneyview, with deployments automating up to 80% of ticket resolutions. Moderne landed five of the top North American banks and grew its customer base 250% in 2024, modernizing millions of lines of legacy code that underpin financial infrastructure.

Store operations teams judge new technology by one test: will this work in every store, every day, alongside what we already run? Autonomous checkout, robotics, and AI planning engines have to plug into POS, inventory, and workforce systems reliably across thousands of locations without disrupting transactions or staffing workflows. Startups built on Azure can support SSO and SAML authentication, role-based access controls, and API integrations with POS, inventory, and ERP systems. Multi-region deployment supports global operations, while uptime SLAs and readiness for vendor risk assessments help IT teams move evaluations forward. This allows retailers to plug new experiences into existing operations instead of rebuilding their technology foundation just to work with a startup.

Large omnichannel retailers sit on ideal conditions for AI innovation: massive transaction volume, rich operational data, and complex store networks where even small efficiency gains scale into material margin and profit. When AI can optimize inventory, labor, and pricing across hundreds or thousands of locations, the impact compounds quickly. Startups built on Azure and Microsoft AI can plug into retailers’ existing data and systems to unlock that value more seamlessly. By unifying POS, inventory, e‑commerce, and sensor data, they can train models that improve demand forecasts, automate replenishment, and prioritize store tasks based on predicted impact. In practice, that can mean AI that keeps key items in stock, reduces wasted labor walks, sharpens pricing and promotions, and shortens checkout journeys, all amplified by the scale of modern omnichannel retail.

Retail is transforming faster than ever. From autonomous stores to AI-powered planning and robotics on the floor, startups built with Microsoft are helping some of the world's largest retailers reinvent customer experience and operations at scale. Auger is bringing agentic AI to supply chains for Meta Reality Labs, Fanatics, and Kimberly-Clark; at Fanatics, roughly 85% of the decisions in the processes Auger manages happen autonomously. AiFi partnered with Microsoft on Smart Store Analytics and deployed autonomous-store technology for retailers including Carrefour, Albert Heijn, and Żabka, as well as the Detroit Lions at Ford Field, where average transaction time fell 50% and per-capita revenue increased 170%. Toolio joined the Microsoft for Startups Pegasus Program and became Microsoft for Retail AI Certified, serving brands including Boll & Branch, MeUndies, Magnolia, and Rothy’s while offering integrations with Dynamics 365 and Power BI. Typeface supports customers including Microsoft, ASICS, Marks & Spencer, and Johnson Controls. General Robotics is extending AI into physical operations with GRID, which it says reduces new-robot onboarding from about one month to as little as two hours.