Accelerate your AI innovation with App Advisor
Have a great idea for an AI app or agent but unsure how to take it to market? Microsoft’s newly released App Advisor provides a clear, guided path from concept to commercialization. Whether you’re shaping an early idea, navigating build decisions, or preparing your solution for the Microsoft commercial marketplace, App Advisor helps streamline each step—reducing friction and increasing confidence. This walkthrough highlights how App Advisor connects the dots: From concept to execution with structured guidance From development to best practices with informed build recommendations From finished product to revenue through proven Marketplace readiness insights If you’re looking to build and sell AI solutions faster and more effectively, this resource is well worth your time. Read the full article: Build an AI app or agent faster to sell on Marketplace with App Advisor | Microsoft Community Hub
Looking for advice on collaborating with complementary Microsoft partners
a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } Hi everyone 👋 My name is Martin Rojze. I’m focused on the Microsoft data platform, with a specialization in Microsoft Fabric and Power BI. My work is centered on helping organizations design, implement, and scale modern analytics and reporting solutions on Azure, with a strong emphasis on real world business outcomes rather than just dashboards. As demand for end to end solutions continues to grow, I’m looking to deepen collaboration with complementary Microsoft partners, for example partners who focus on Dynamics 365 or Business Central Data engineering, data science, or AI App development including Power Apps, custom apps, or ISVs Security, governance, or change management I’d really appreciate advice from partners who have successfully built co sell or referral relationships, specifically What has worked and what has not when partnering with other Microsoft partners How you structure collaboration so it’s mutually beneficial and scalable Tips on aligning around go to market, co selling, or delivery without stepping on each other’s toes If you’re a partner interested in collaborating around Fabric and Power BI led analytics engagements, or if you’re willing to share lessons learned, I’d love to connect and learn from your experience. Thanks in advance and looking forward to the discussion. MartinDeveloping AI solutions on Microsoft Azure blueprint survey opportunity
Greetings! Microsoft is considering a credential for Developing AI solutions on Microsoft Azure, and we need your input through our exam blueprinting survey. The blueprint determines how many questions each skill in the exam will be assigned. Please complete the online survey by February 23, 2026. Please also feel free to forward the survey to any colleagues you consider subject matter experts for this certification. If you have any questions, feel free to contact John Sowles at josowles@microsoft.com or Don Tanedo at dtanedo@microsoft.com. Developing AI solutions on Microsoft Azure blueprint survey link: https://microsoftlearning.co1.qualtrics.com/jfe/form/SV_easBwCCKSwxGyZ8Build an AI app or agent faster to sell on Marketplace with App Advisor
Jump right to the step-by-step curated guidance for building a well-architected app in App Advisor The development of AI apps and agents is moving fast. The data backs it up: The AI platform of choice: 90% of Fortune 500 companies use Microsoft AI, Development lifecycles speed up: Teams see up to 46% faster development with Azure, Companies realize great ROI: AI apps and agents realize an average of 90% ROI. The opportunity is real, but speed alone isn’t enough. To turn momentum into a scalable, sellable solution, software companies need a clear path from idea to architecture to Microsoft Marketplace readiness. How to build an AI app or agent and sell it on Marketplace Microsoft Foundry provides the platform to build AI apps and agents. App Advisor provides the structure, guiding you through the decisions that matter early. So, you can move fast without creating rework later. When paired with focused build and alignment checklists, App Advisor helps you ship faster and prepare your solution for the Marketplace from day one. Here’s how App Advisor, paired with material developed from the popular AI Envisioning sessions, helps you build with speed and Marketplace readiness in mind. App Advisor gives you a clear starting point to build AI apps and agents App Advisor is tailored specifically to remove ambiguity. Instead of guessing where to begin, you’re guided through key build decisions, based on five critical pillars: Security Reliability Cost optimization Operational excellence Performance efficiency These pillars are more than theory. They help you and your team think through pricing models, identity and access, resiliency, scaling, and operational integration early: before code hardens and changes get expensive. By anchoring your build in these principles, you create a foundation that supports both rapid development and future Marketplace requirements. Design well, build fast: Create an Agent MVP in 30 days After your architecture is defined, the next challenge is execution speed. The Create an Agent MVP in 30 Days checklist turns Microsoft Foundry best practices into an actionable build plan. It’s organized around the same well-architected pillars, making it easy to move from design to implementation without losing alignment. This checklist helps you: Embed security controls like encryption, RBAC, and managed identity from day one, Design for reliability with stateless services, health checks, and graceful fallback, Right-size performance and compute so you don’t overbuild the MVP, Control costs early with monitoring, budgets, and automation, Set up DevOps and CI/CD to support fast iteration. The result is a working agent that’s intentionally scoped, production-aware, and easier to evolve into a commercial offering. Built to move fast, ready to be sold at scale Used together, App Advisor, the 30-day to an MVP checklist, and the AI Envisioning sessions give you a repeatable path: Start with architecture clarity, Build an MVP quickly and responsibly, Stay aligned on Marketplace outcomes. You’re not just shipping an AI app or agent. You’re building a solution designed to be deployed, sold, and supported for your target customer. And designing well with solid core principles can help build a solid foundation for your Marketplace success. Ready to build faster with Microsoft Foundry? Explore App Advisor for step-by-step guidance to quickly design well-architected apps. Want to get development templates to start designing in minutes? View the Quick-Start Development Toolkit library for AI apps and agents. See the difference when you build with the proven framework of Microsoft Foundry and selling well-architected apps and agents in Marketplace.Agents League: Build, Learn, and Level Up Your AI Skills
We're inviting the next generation of developers to join Agents League, running February 16-27. It's a two-week challenge where you'll build AI agents using production-ready tools, learn from live coding sessions, and get feedback directly from Microsoft product teams. We've put together starter kits for each track to help you get up and running quickly that also includes requirements and guidelines. Whether you want to explore what GitHub Copilot can do beyond autocomplete, build reasoning agents on Microsoft Foundry, or create enterprise integrations for Microsoft 365 Copilot, we have a track for you. Important: Register first to be eligible for prizes and your digital badge. Without registration, you won't qualify for awards or receive a badge when you submit. What Is Agents League? It's a 2-week competition where you learn by doing: 📽️ Live coding battles – Watch experts compete in real-time and explain their thinking 💻 Build at your pace – Two weeks to work on your project 💬 Get help on Discord – AMAs, community support, and a friendly crowd to cheer you on 🏆 Win prizes – $500 per track, GitHub Copilot Pro subscriptions, and digital badges for everyone who submits The Three Tracks 🎨 Creative Apps — Build with GitHub Copilot (Chat, CLI, or SDK) 🧠 Reasoning Agents — Build with Microsoft Foundry 💼 Enterprise Agents — Build with M365 Agents Toolkit (or Copilot Studio) More details on each track below, or jump straight to the starter kits. The Schedule Agents League starts on February 16th and runs through February 27th. Within 2 weeks, we host live battles on Reactor and AMA sessions on Discord. Week 1: Live Battles (Feb 17-19) We're kicking off with live coding battles streamed on Microsoft Reactor. Watch experienced developers compete in real-time, explaining their approach and architectural decisions as they go. Tue Feb 17, 9 AM PT — 🎨 Creative Apps battle Wed Feb 18, 9 AM PT — 🧠 Reasoning Agents battle Thu Feb 19, 9 AM PT — 💼 Enterprise Agents battle All sessions are recorded, so you can watch on your own schedule. Week 2: Build + AMAs (Feb 24-26) This is your time to build and ask questions on Discord. The async format means you work when it suits you, evenings, weekends, whatever fits your schedule. We're also hosting AMAs on Discord where you can ask questions directly to Microsoft experts and product teams: Tue Feb 24, 9 AM PT — 🎨 Creative Apps AMA Wed Feb 25, 9 AM PT — 🧠 Reasoning Agents AMA Thu Feb 26, 9 AM PT — 💼 Enterprise Agents AMA Bring your questions, get help when you're stuck, and share what you're building with the community. Pick Your Track We've created a starter kit for each track with setup guides, project ideas, and example scenarios to help you get started quickly. 🎨 Creative Apps Tool: GitHub Copilot (Chat, CLI, or SDK) Build innovative, imaginative applications that showcase the potential of AI-assisted development. All application types are welcome, web apps, CLI tools, games, mobile apps, desktop applications, and more. The starter kit walks you through GitHub Copilot's different modes and provides prompting tips to get the best results.View the Creative Apps starter kit. 🧠 Reasoning Agents Tool: Microsoft Foundry (UI or SDK) and/or Microsoft Agent Framework Build a multi-agent system that leverages advanced reasoning capabilities to solve complex problems. This track focuses on agents that can plan, reason through multi-step problems, and collaborate. The starter kit includes architecture patterns, reasoning strategies (planner-executor, critic/verifier, self-reflection), and integration guides for tools and MCP servers. View the Reasoning Agents starter kit. 💼 Enterprise Agents Tool: M365 Agents Toolkit or Copilot Studio Create intelligent agents that extend Microsoft 365 Copilot to address real-world enterprise scenarios. Your agent must work on Microsoft 365 Copilot Chat. Bonus points for: MCP server integration, OAuth security, Adaptive Cards UI, connected agents (multi-agent architecture). View the Enterprise Agents starter kit. Prizes & Recognition To be eligible for prizes and your digital badge, you must register before submitting your project. Category Winners ($500 each): 🎨 Creative Apps winner 🧠 Reasoning Agents winner 💼 Enterprise Agents winner GitHub Copilot Pro subscriptions: Community Favorite (voted by participants on Discord) Product Team Picks (selected by Microsoft product teams) Everyone who registers and submits a project wins: A digital badge to showcase their participation. Beyond the prizes, every participant gets feedback from the teams who built these tools, a valuable opportunity to learn and improve your approach to AI agent development. Why This Matters AI development is where the opportunities are right now. Building with GitHub Copilot, Microsoft Foundry, and M365 Agents Toolkit gives you: A real project for your portfolio Hands-on experience with production-grade tools Connections with developers from around the world Whether you're looking for your first internship, exploring AI, or just want to build something cool, this is two weeks well spent. How to Get Started Register first — This is required to be eligible for prizes and to receive your digital badge. Without registration, your submission won't qualify for awards or a badge. Pick a track — Choose one track. Explore the starter kits to help you decide. Watch the battles — See how experienced developers approach these challenges. Great for learning even if you're still deciding whether to compete. Build your project — You have until Feb 27. Work on your own schedule. Submit via GitHub — Open an issue using the project submission template. Join us on Discord — Get help, share your progress, and vote for your favorite projects on Discord. Links Register: https://aka.ms/agentsleague/register Starter Kits: https://github.com/microsoft/agentsleague/starter-kits Discord: https://aka.ms/agentsleague/discord Live Battles: https://aka.ms/agentsleague/battles Submit Project: Project submission templateBuilding with Azure OpenAI Sora: A Complete Guide to AI Video Generation
In this comprehensive guide, we'll explore how to integrate both Sora 1 and Sora 2 models from Azure OpenAI Service into a production web application. We'll cover API integration, request body parameters, cost analysis, limitations, and the key differences between using Azure AI Foundry endpoints versus OpenAI's native API. Table of Contents Introduction to Sora Models Azure AI Foundry vs. OpenAI API Structure API Integration: Request Body Parameters Video Generation Modes Cost Analysis per Generation Technical Limitations & Constraints Resolution & Duration Support Implementation Best Practices Introduction to Sora Models Sora is OpenAI's groundbreaking text-to-video model that generates realistic videos from natural language descriptions. Azure AI Foundry provides access to two versions: Sora 1: The original model focused primarily on text-to-video generation with extensive resolution options (480p to 1080p) and flexible duration (1-20 seconds) Sora 2: The enhanced version with native audio generation, multiple generation modes (text-to-video, image-to-video, video-to-video remix), but more constrained resolution options (720p only in public preview) Azure AI Foundry vs. OpenAI API Structure Key Architectural Differences Sora 1 uses Azure's traditional deployment-based API structure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/... Parameters: Uses Azure-specific naming like n_seconds, n_variants, separate width/height fields Job Management: Uses /jobs/{id} for status polling Content Download: Uses /video/generations/{generation_id}/content/video Sora 2 adapts OpenAI's v1 API format while still being hosted on Azure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/videos Parameters: Uses OpenAI-style naming like seconds (string), size (combined dimension string like "1280x720") Job Management: Uses /videos/{video_id} for status polling Content Download: Uses /videos/{video_id}/content Why This Matters? This architectural difference requires conditional request formatting in your code: const isSora2 = deployment.toLowerCase().includes('sora-2'); if (isSora2) { requestBody = { model: deployment, prompt, size: `${width}x${height}`, // Combined format seconds: duration.toString(), // String type }; } else { requestBody = { model: deployment, prompt, height, // Separate dimensions width, n_seconds: duration.toString(), // Azure naming n_variants: variants, }; } API Integration: Request Body Parameters Sora 1 API Parameters Standard Text-to-Video Request: { "model": "sora-1", "prompt": "Wide shot of a child flying a red kite in a grassy park, golden hour sunlight, camera slowly pans upward.", "height": "720", "width": "1280", "n_seconds": "12", "n_variants": "2" } Parameter Details: model (String, Required): Your Azure deployment name prompt (String, Required): Natural language description of the video (max 32000 chars) height (String, Required): Video height in pixels width (String, Required): Video width in pixels n_seconds (String, Required): Duration (1-20 seconds) n_variants (String, Optional): Number of variations to generate (1-4, constrained by resolution) Sora 2 API Parameters Text-to-Video Request: { "model": "sora-2", "prompt": "A serene mountain landscape with cascading waterfalls, cinematic drone shot", "size": "1280x720", "seconds": "12" } Image-to-Video Request (uses FormData): const formData = new FormData(); formData.append('model', 'sora-2'); formData.append('prompt', 'Animate this image with gentle wind movement'); formData.append('size', '1280x720'); formData.append('seconds', '8'); formData.append('input_reference', imageFile); // JPEG/PNG/WebP Video-to-Video Remix Request: Endpoint: POST .../videos/{video_id}/remix Body: Only { "prompt": "your new description" } The original video's structure, motion, and framing are reused while applying the new prompt Parameter Details: model (String, Optional): Your deployment name prompt (String, Required): Video description size (String, Optional): Either "720x1280" or "1280x720" (defaults to "720x1280") seconds (String, Optional): "4", "8", or "12" (defaults to "4") input_reference (File, Optional): Reference image for image-to-video mode remix_video_id (String, URL parameter): ID of video to remix Video Generation Modes 1. Text-to-Video (Both Models) The foundational mode where you provide a text prompt describing the desired video. Implementation: const response = await fetch(endpoint, { method: 'POST', headers: { 'Content-Type': 'application/json', 'api-key': apiKey, }, body: JSON.stringify({ model: deployment, prompt: "A train journey through mountains with dramatic lighting", size: "1280x720", seconds: "12", }), }); Best Practices: Include shot type (wide, close-up, aerial) Describe subject, action, and environment Specify lighting conditions (golden hour, dramatic, soft) Add camera movement if desired (pans, tilts, tracking shots) 2. Image-to-Video (Sora 2 Only) Generate a video anchored to or starting from a reference image. Key Requirements: Supported formats: JPEG, PNG, WebP Image dimensions must exactly match the selected video resolution Our implementation automatically resizes uploaded images to match Implementation Detail: // Resize image to match video dimensions const targetWidth = parseInt(width); const targetHeight = parseInt(height); const resizedImage = await resizeImage(inputReference, targetWidth, targetHeight); // Send as multipart/form-data formData.append('input_reference', resizedImage); 3. Video-to-Video Remix (Sora 2 Only) Create variations of existing videos while preserving their structure and motion. Use Cases: Change weather conditions in the same scene Modify time of day while keeping camera movement Swap subjects while maintaining composition Adjust artistic style or color grading Endpoint Structure: POST {base_url}/videos/{original_video_id}/remix?api-version=2024-08-01-preview Implementation: let requestEndpoint = endpoint; if (isSora2 && remixVideoId) { const [baseUrl, queryParams] = endpoint.split('?'); const root = baseUrl.replace(/\/videos$/, ''); requestEndpoint = `${root}/videos/${remixVideoId}/remix${queryParams ? '?' + queryParams : ''}`; } Cost Analysis per Generation Sora 1 Pricing Model Base Rate: ~$0.05 per second per variant at 720p Resolution Scaling: Cost scales linearly with pixel count Formula: const basePrice = 0.05; const basePixels = 1280 * 720; // Reference resolution const currentPixels = width * height; const resolutionMultiplier = currentPixels / basePixels; const totalCost = basePrice * duration * variants * resolutionMultiplier; Examples: 720p (1280×720), 12 seconds, 1 variant: $0.60 1080p (1920×1080), 12 seconds, 1 variant: $1.35 720p, 12 seconds, 2 variants: $1.20 Sora 2 Pricing Model Flat Rate: $0.10 per second per variant (no resolution scaling in public preview) Formula: const totalCost = 0.10 * duration * variants; Examples: 720p (1280×720), 4 seconds: $0.40 720p (1280×720), 12 seconds: $1.20 720p (720×1280), 8 seconds: $0.80 Note: Since Sora 2 currently only supports 720p in public preview, resolution doesn't affect cost, only duration matters. Cost Comparison Scenario Sora 1 (720p) Sora 2 (720p) Winner 4s video $0.20 $0.40 Sora 1 12s video $0.60 $1.20 Sora 1 12s + audio N/A (no audio) $1.20 Sora 2 (unique) Image-to-video N/A $0.40-$1.20 Sora 2 (unique) Recommendation: Use Sora 1 for cost-effective silent videos at various resolutions. Use Sora 2 when you need audio, image/video inputs, or remix capabilities. Technical Limitations & Constraints Sora 1 Limitations Resolution Options: 9 supported resolutions from 480×480 to 1920×1080 Includes square, portrait, and landscape formats Full list: 480×480, 480×854, 854×480, 720×720, 720×1280, 1280×720, 1080×1080, 1080×1920, 1920×1080 Duration: Flexible: 1 to 20 seconds Any integer value within range Variants: Depends on resolution: 1080p: Variants disabled (n_variants must be 1) 720p: Max 2 variants Other resolutions: Max 4 variants Concurrent Jobs: Maximum 2 jobs running simultaneously Job Expiration: Videos expire 24 hours after generation Audio: No audio generation (silent videos only) Sora 2 Limitations Resolution Options (Public Preview): Only 2 options: 720×1280 (portrait) or 1280×720 (landscape) No square formats No 1080p support in current preview Duration: Fixed options only: 4, 8, or 12 seconds No custom durations Defaults to 4 seconds if not specified Variants: Not prominently supported in current API documentation Focus is on single high-quality generations with audio Concurrent Jobs: Maximum 2 jobs (same as Sora 1) Job Expiration: 24 hours (same as Sora 1) Audio: Native audio generation included (dialogue, sound effects, ambience) Shared Constraints Concurrent Processing: Both models enforce a limit of 2 concurrent video jobs per Azure resource. You must wait for one job to complete before starting a third. Job Lifecycle: queued → preprocessing → processing/running → completed Download Window: Videos are available for 24 hours after completion. After expiration, you must regenerate the video. Generation Time: Typical: 1-5 minutes depending on resolution, duration, and API load Can occasionally take longer during high demand Resolution & Duration Support Matrix Sora 1 Support Matrix Resolution Aspect Ratio Max Variants Duration Range Use Case 480×480 Square 4 1-20s Social thumbnails 480×854 Portrait 4 1-20s Mobile stories 854×480 Landscape 4 1-20s Quick previews 720×720 Square 4 1-20s Instagram posts 720×1280 Portrait 2 1-20s TikTok/Reels 1280×720 Landscape 2 1-20s YouTube shorts 1080×1080 Square 1 1-20s Premium social 1080×1920 Portrait 1 1-20s Premium vertical 1920×1080 Landscape 1 1-20s Full HD content Sora 2 Support Matrix Resolution Aspect Ratio Duration Options Audio Generation Modes 720×1280 Portrait 4s, 8s, 12s ✅ Yes Text, Image, Video Remix 1280×720 Landscape 4s, 8s, 12s ✅ Yes Text, Image, Video Remix Note: Sora 2's limited resolution options in public preview are expected to expand in future releases. Implementation Best Practices 1. Job Status Polling Strategy Implement adaptive backoff to avoid overwhelming the API: const maxAttempts = 180; // 15 minutes max let attempts = 0; const baseDelayMs = 3000; // Start with 3 seconds while (attempts < maxAttempts) { const response = await fetch(statusUrl, { headers: { 'api-key': apiKey }, }); if (response.status === 404) { // Job not ready yet, wait longer const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; continue; } const job = await response.json(); // Check completion (different status values for Sora 1 vs 2) const isCompleted = isSora2 ? job.status === 'completed' : job.status === 'succeeded'; if (isCompleted) break; // Adaptive backoff const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; } 2. Handling Different Response Structures Sora 1 Video Download: const generations = Array.isArray(job.generations) ? job.generations : []; const genId = generations[0]?.id; const videoUrl = `${root}/${genId}/content/video`; Sora 2 Video Download: const videoUrl = `${root}/videos/${jobId}/content`; 3. Error Handling try { const response = await fetch(endpoint, fetchOptions); if (!response.ok) { const error = await response.text(); throw new Error(`Video generation failed: ${error}`); } // ... handle successful response } catch (error) { console.error('[VideoGen] Error:', error); // Implement retry logic or user notification } 4. Image Preprocessing for Image-to-Video Always resize images to match the target video resolution: async function resizeImage(file: File, targetWidth: number, targetHeight: number): Promise<File> { return new Promise((resolve, reject) => { const img = new Image(); const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); img.onload = () => { canvas.width = targetWidth; canvas.height = targetHeight; ctx.drawImage(img, 0, 0, targetWidth, targetHeight); canvas.toBlob((blob) => { if (blob) { const resizedFile = new File([blob], file.name, { type: file.type }); resolve(resizedFile); } else { reject(new Error('Failed to create resized image blob')); } }, file.type); }; img.onerror = () => reject(new Error('Failed to load image')); img.src = URL.createObjectURL(file); }); } 5. Cost Tracking Implement cost estimation before generation and tracking after: // Pre-generation estimate const estimatedCost = calculateCost(width, height, duration, variants, soraVersion); // Save generation record await saveGenerationRecord({ prompt, soraModel: soraVersion, duration: parseInt(duration), resolution: `${width}x${height}`, variants: parseInt(variants), generationMode: mode, estimatedCost, status: 'queued', jobId: job.id, }); // Update after completion await updateGenerationStatus(jobId, 'completed', { videoId: finalVideoId }); 6. Progressive User Feedback Provide detailed status updates during the generation process: const statusMessages: Record<string, string> = { 'preprocessing': 'Preprocessing your request...', 'running': 'Generating video...', 'processing': 'Processing video...', 'queued': 'Job queued...', 'in_progress': 'Generating video...', }; onProgress?.(statusMessages[job.status] || `Status: ${job.status}`); Conclusion Building with Azure OpenAI's Sora models requires understanding the nuanced differences between Sora 1 and Sora 2, both in API structure and capabilities. Key takeaways: Choose the right model: Sora 1 for resolution flexibility and cost-effectiveness; Sora 2 for audio, image inputs, and remix capabilities Handle API differences: Implement conditional logic for parameter formatting and status polling based on model version Respect limitations: Plan around concurrent job limits, resolution constraints, and 24-hour expiration windows Optimize costs: Calculate estimates upfront and track actual usage for better budget management Provide great UX: Implement adaptive polling, progressive status updates, and clear error messages The future of AI video generation is exciting, and Azure AI Foundry provides production-ready access to these powerful models. As Sora 2 matures and limitations are lifted (especially resolution options), we'll see even more creative applications emerge. Resources: Azure AI Foundry Sora Documentation OpenAI Sora API Reference Azure OpenAI Service Pricing This blog post is based on real-world implementation experience building LemonGrab, my AI video generation platform that integrates both Sora 1 and Sora 2 through Azure AI Foundry. The code examples are extracted from production usage.Partner Case Study | Mead Johnson Nutrition
In the race to innovate, global enterprises aren’t usually held back by a lack of ideas but rather by the friction hidden in their data. Legacy SAP systems, siloed data storage environments, and inconsistent data structures can quietly slow operations, including reporting, forecasting, compliance, and even cutting-edge AI adoption. This friction can be especially acute in the retail life sciences and consumer health sectors, where any new products must adhere to strict standards of safety and scientific rigor, all at an enterprise scale. In these data-intensive environments, the stakes are high, and the pressure to innovate safely and without business disruption is even higher. Healthcare and consumer health organizations need a partner who can help them move fast while respecting the boundaries, systems, and regulations that keep them compliant. In other words, they need a partner with the speed and precision of a tiger—and Tiger Analytics is true to their namesake. Founded in 2011, Tiger Analytics is an innovator in AI and data science consulting with a team of more than 6,200. A Microsoft partner with specializations in Analytics and AI and Machine Learning, the team helps enterprises around the world modernize complex data ecosystems at scale so they can harness insights for real-world innovation. With deep experience in SAP, data engineering, and Azure cloud services, they’ve built a reputation for solving high-stakes data challenges with clarity and speed. And it was these capabilities that made Tiger Analytics an ideal option when global pediatric nutrition leader Mead Johnson Nutrition (MJN) decided to transform their digital operations and lay the foundation for faster, more efficient, and scalable enterprise AI. Modernizing data architecture to fuel smarter innovation With more than a century of experience developing science-based formulas for infants and children, MJN serves millions of families in highly regulated markets across North America, Asia, and Latin America. The scale of their operation makes any digital transformation a significant undertaking. So when they initiated a major effort to modernize their enterprise resource planning—which included migrating from SAP ECC to SAP S/4HANA—there was some inherent risk involved. The switch disrupted the existing data replication layer that fed analytics and reporting systems across the enterprise. To maintain business continuity, MJN needed to rapidly rebuild and enhance this layer. MJN already had an established partnership with Tiger Analytics, having worked with them on various data engineering initiatives. MJN brought Tiger Analytics in again to help design, build, and operationalize the new replication layer, enabling seamless data flow from the upgraded S/4HANA system to MJN’s analytics platform. MJN outlined three key strategic goals they needed the solution to support: Real-time replication from S/4HANA into the DnA Delta Lakehouse for advanced analytics, AI and machine learning use cases, and enterprise-wide reporting. Clean and secure nutrition data delivery from SAP S/4 to the Global EDAP team, which manages centralized analytics and data processing across business units. A smooth, timely transition to ensure business teams retained access to business-critical SAP tables. With MJN's goals clearly articulated and the partnership already well established, Tiger Analytics was ready to get to work. Continue reading here Explore all case studies or submit your own Subscribe to case studies tag to follow all new case study posts. Don't forget to follow this blog to receive email notifications of new stories!Demystifying Microsoft Marketplace & Azure IP Co‑Sell expectations
Get ready for a practical, insider‑focused session designed to demystify what drives real success in Microsoft Marketplace and Microsoft Azure IP co‑sell. Join us on February 25th at 8:30 AM PST to hear directly from guest expert Barbara Treviño, Director of Strategic Partnerships and Alliances at Labra, as she highlights the signals Microsoft looks for in strong submissions and reveals how high‑performing software development companies set themselves apart. This session gives you clear guidance on aligning your solution, documentation, and customer proof to accelerate approvals and maximize go‑to‑market impact—setting you up to turn Marketplace and co‑sell into true growth drivers for your business. Registration is not required. Learn how you can attend and receive reminders for the session here: Inside Azure IP co-sell: What high-performing software developers do differently - Microsoft Marketplace Community All sessions are recorded, and the link above can be used to watch the recording once the session date has passed. Check our Marketplace trainings and events calendar | Microsoft Community Hub for additional upcoming Microsoft Marketplace events and recordings of past sessions.
