For years, insurers have experimented with digital initiatives, but the pace of disruption has accelerated. Legacy models can’t keep up with rising risks, evolving customer expectations, and operational pressures. The question isn’t whether insurers will transform, but rather how fast they can adapt.

Technologies like AI, advanced analytics, and embedded solutions have moved from emerging concepts to essential capabilities for competitive advantage. Earlier this year, we highlighted these opportunities in our Top 5 Digital Trends for Insurance.

As we gear up for the world’s largest event for insurance innovation in October, ITC Vegas, it’s clear these trends are shaping the conversations that matter most. Here’s a closer look at three that are leading the way.

1. Make AI Your Growth Engine

Artificial intelligence is a core enabler of insurance innovation. It’s powering efficiency and elevating customer experiences across the value chain. From underwriting to claims, AI enables real-time decisions, sharpens risk modeling, and delivers personalized interactions at scale. Generative AI builds on this foundation by accelerating content creation, enabling smarter agent support, and transforming customer engagement. Together, these capabilities thrive on modern, cloud-native platforms designed for speed and scalability.

Why Leaders Should Act Now:

AI creates value when it’s embedded in workflows. Focus on the high-impact domains that accelerate outcomes: underwriting, claims, and distribution. Research shows early AI adopters are already seeing measurable results:

• New-agent success and sales conversion rates increased up to 20%
• Premium growth boosted by as much as 15%
• New customer onboarding costs reduced up to 40%

We help clients advance AI capabilities through virtual assistants, generative interfaces, agentic frameworks, and product development, enhancing team velocity by integrating AI team members.

Read More: Empowering the Modern Insurance Agent

2. Personalize Every Moment

Today’s policyholders expect the same level of personalization they receive from other industries like retail and streaming platforms. By leveraging AI and advanced analytics, insurers can move beyond broad segments to anticipate needs, remove friction, and tailor products and pricing in the moments that matter.

Forbes highlights three key pillars of modern personalization critical for insurers aiming to deliver tailored experiences: data, intent signals, and artificial intelligence. At ITC, these principles are front and center as insurers explore how to meet expectations and unlock new revenue streams, without adding complexity.

Why Leaders Should Act Now:

Personalization isn’t just about customer experience—it’s a growth strategy. Research shows over 70% of consumers expect personalized interactions, and more than three-quarters feel frustrated when they don’t get them. Insurers that utilize AI to anticipate needs and simplify choices can earn trust and loyalty faster than those who don’t.

Success In Action: Proving Rapid Value and Creating Better Member Experiences

3. Meet Customers at the Point of Need

Embedded insurance is moving into everyday moments, and research shows it’s on a massive growth trajectory. Global P&C embedded sales are projected to reach as high as $700 billion by 2030, including $70 billion in the U.S. alone. By meeting customers where decisions happen, carriers can create seamless experiences, new revenue streams, and stronger brand visibility—while offering convenience, transparency, and choice.

Insurers that embrace ecosystems will expand their reach and relevance as consumer expectations and engagement continually shift. Agencies will continue to play a critical role in navigating difficult underwriting conditions by tailoring policy coverages and providing transparency, which requires that they have access to modern sales and servicing tools. It’s a prominent theme that’s echoed throughout ITC sessions this year.

Why Leaders Should Act Now:

AI amplifies embedded strategies by enabling real-time pricing, risk assessment, and personalized offers within those touchpoints. What matters most is making the “yes” simple: clear options, plain language, and confidence about what’s covered. Together, embedded ecosystems and AI-driven insights help insurers deliver relevance at scale when and where consumers need it.

You May Also Enjoy: Commerce Experiences and the Rise of Digital-First Insurance

Lead the Insurance Evolution With AI-First Transformation

The insurance industry is entering uncharted territory. Those who act decisively and swiftly to leverage AI, embrace embedded ecosystems, and personalize every moment will lead the curve in the next era of insurance.

As the industry gathers at events like ITC Vegas, these conversations come to life. Expect AI to be the common thread across underwriting, claims, distribution, and customer experience. If you’re attending ITC at Mandalay Bay in October, schedule a meeting with our team to explore how we help insurers turn disruption into opportunity.

Carriers and brokers count on us to help modernize, innovate, and win in an increasingly competitive marketplace. Our solutions power personalized omnichannel experiences and optimize performance across the enterprise.

• Business Transformation: Activate strategy and innovation ​within the insurance ecosystem.​
• Modernization: Optimize technology to boost agility and ​efficiency across the value chain.​
• Data + Analytics: Power insights and accelerate ​underwriting and claims decision-making.​
• Customer Experience: Ease and personalize experiences ​for policyholders and producers.​

We are trusted by leading technology partners and consistently mentioned by analysts. Discover why we have been trusted by 13 of the 20 largest P&C firms and 11 of the 20 largest annuity carriers. Explore our insurance expertise and contact us to learn more.

My 30-year technology career has taught me many things…and one big thing: the companies that treat technology as a cost center are the ones that get blindsided. In manufacturing, that blindside is already here — and it’s wearing the name tag “AI.”

For decades, manufacturers have been locked into rigid systems, long upgrade cycles, and siloed data. The result? Operations that run on yesterday’s insights while competitors are making tomorrow’s moves. Sound familiar? It’s the same trap traditional IT outsourcing fell into — and it’s just as deadly in the age of smart manufacturing.

The AI Advantage in Manufacturing

Oracle Fusion AI for Manufacturing Smart Operations isn’t just another software upgrade. It’s a shift from reactive to predictive, from siloed to synchronized. Think:

• Real-time anomaly detection that flags quality issues before they hit the line.
• Predictive maintenance that slashes downtime and extends asset life.
• Intelligent scheduling that adapts to supply chain disruptions in minutes, not weeks.
• Embedded analytics that turn every operator, planner, and manager into a decision-maker armed with live data.

This isn’t about replacing people — it’s about giving them superpowers. Read more from Oracle here.

Proof in Action: Roeslein & Associates

If you want to see what this looks like in the wild, look at Roeslein & Associates. They were running on disparate, outdated legacy systems — the kind that make global process consistency a pipe dream. Perficient stepped in and implemented Oracle Fusion Cloud Manufacturing with Project Driven Supply Chain, plus full Financial and Supply Chain Management suites. The result?

• A global solution template that can be rolled out anywhere in the business.
• A redesigned enterprise structure to track profits across business units.
• Standardized manufacturing processes that still flex for highly customized demand.
• Integrated aftermarket parts ordering and manufacturing flows.
• Seamless connections between Fusion, labor capture systems, and eCommerce.

That’s not just “going live” — that’s rewiring the operational nervous system for speed, visibility, and scale.

Why Standing Still is Riskier Than Moving Fast

In my words, “true innovation is darn near impossible” when you’re chained to legacy thinking. The same applies here: if your manufacturing ops are running on static ERP data and manual interventions, you’re already losing ground to AI‑driven competitors who can pivot in real time.

Oracle Fusion Cloud with embedded AI is the equalizer. A mid‑sized manufacturer with the right AI tools can outmaneuver industry giants still stuck in quarterly planning cycles.

Where Perficient Comes In

Perficient’s Oracle team doesn’t just implement software — they architect transformation. With deep expertise in Oracle Manufacturing Cloud, Supply Chain Management, and embedded Fusion AI solutions, they help you:

• Integrate AI into existing workflows without blowing up your operations.
• Optimize supply chain visibility from raw materials to customer delivery.
• Leverage IoT and machine learning for continuous process improvement.
• Scale securely in the cloud while keeping compliance and governance in check.

They’ve done it for global manufacturers, and they can do it for you — faster than you think.

The Call to Action

If you believe your manufacturing operations are immune to disruption, history says otherwise. The companies that win will be the ones that treat AI not as a pilot project, but as the new operating system for their business.

Rather than letting new entrants disrupt your position, take initiative and lead the charge—make them play catch-up.

Well, that was a lot to unpack. The Apple event today, announcing iOS 26 and the iPhone 17, truly lived up to the “Awe Dropping” invitation, and not just because of the new iPhone 17 Air’s ridiculously thin design. While the new 24MP selfie camera, the upgraded 48MP Telephoto lens on the Pro models, and the “Liquid Glass” UI are certainly head-turners, the real narrative is about something far more fundamental.

This event wasn’t just about a new phone; it was about the official start of a new mobile era. Apple is openly acknowledging that the old paradigm—the one where we’re constantly chasing down apps and fighting off a barrage of distracting push notifications—is finally being put to rest. And honestly, it’s about time. Our digital lives have become a frantic game of Whack-A-Mole, tapping icons and dismissing alerts just to get a single task done.

What we saw today with iOS 26 isn’t a simple evolution; it’s a profound re-architecture of how the mobile operating system works, and it’s a brilliant example of creative technology in action. It’s built on an anticipatory design model, where the system itself becomes a proactive companion, anticipating what we need before we even ask. The new on-device “Visual Intelligence” that lets you interact with anything on your screen is a perfect metaphor for this shift—it’s less about launching a specific app and more about the device just knowing what to do.

This all hinges on the unique convergence of four foundational pillars that Apple has been building for years:

1. On-device AI: The new A19 Pro chip with its beefed-up Neural Engine isn’t just for faster processing; it’s the engine for this new, context-aware intelligence. All that real-time analysis of your data happens on the device, keeping your personal information private and secure, which is a key differentiator in today’s privacy-conscious landscape.
2. Conversational UIs: The deeper integration of natural language requests means we’re moving toward a system where we can simply ask our phone to perform complex, multi-step tasks across different apps, all while the system seamlessly orchestrates the workflow.
3. App Abstraction: This is the big one. We’re seeing the dissolution of the app as a silo. Instead, apps are becoming a collection of “components” or “intelligent actions” that can be called upon system-wide. The ability for the new “Live Translation” feature to pull data from Messages and the Phone app is a clear sign that this new framework is here to stay.
4. Security and Privacy: The announcement last year of the new “Private Cloud Compute” isn’t just a feature; it’s a strategic pillar. It shows that Apple is doubling down on its privacy-first ethos, demonstrating that powerful AI can be delivered without sacrificing the trust of its users.

The competitive landscape is shifting in a fundamental way. It’s no longer about which company can create a single, all-encompassing super-app. The new battleground is how well a business can integrate its functionality and value into this ambient, proactive mobile experience. The winners will be those who can transition from a transactional “app” model to a service-based “companion” model—providing continuous, frictionless value that makes our lives easier, not more cluttered. The companies that will win are proactively establishing/defining the capabilities and data required to deliver these seamless mobile experiences. When the game changes on the front-end, the back end needs to pivot.

The iPhone 17 and iOS 26 aren’t just incremental updates. They represent a significant turning point in the industry. It’s a move from a mobile world we have to consciously navigate to one that feels more like a seamless extension of ourselves. And for those of us in the business of creative digital engagement, that’s the most exciting and awe-dropping announcement of all.

In today’s fast-paced digital workplace, agility, scalability, and collaboration aren’t just nice to have—they’re business-critical. If your organization is still on Microsoft SharePoint On-Premises, now is the time to make the move to SharePoint Online. Here’s why this isn’t just a technology upgrade—it’s a strategic leap forward.

1. Work Anywhere, Without Barriers

SharePoint Online empowers your workforce with secure access to content from virtually anywhere. Whether your team is remote, hybrid, or on the go, they can collaborate in real time without being tethered to a corporate network or VPN.

2. Always Up to Date

Forget about manual patching and version upgrades. SharePoint Online is part of Microsoft 365, which means you automatically receive the latest features, security updates, and performance improvements—without the overhead of managing infrastructure.

3. Reduce Costs and Complexity

Maintaining on-premises servers is expensive and resource-intensive. By moving to SharePoint Online, you eliminate hardware costs, reduce IT overhead, and streamline operations. Plus, Microsoft handles the backend, so your team can focus on innovation instead of maintenance.

4. Enterprise-Grade Security and Compliance

Microsoft invests heavily in security, offering built-in compliance tools, data loss prevention, and advanced threat protection. SharePoint Online is designed to meet global standards and industry regulations, giving you peace of mind that your data is safe.

5. Seamless Integration with Microsoft 365

SharePoint Online integrates effortlessly with Microsoft Teams, OneDrive, Power Automate, and Power BI—enabling smarter workflows, better insights, and more connected experiences across your organization.

6. Scalability for the Future

Whether you’re a small business or a global enterprise, SharePoint Online scales with your needs. You can easily add users, expand storage, and adapt to changing business demands without worrying about infrastructure limitations.

Why Perficient for Your SharePoint Online Migration 

• 35% boost in collaboration efficiency
• Up to 60% cost savings per user
• 73% reduction in data breach risk
• 100+ IT hours saved each month

Perficient is proud to be included in the IDC Market Glance: Digital Business Professional Services, 3Q25, (Doc # US52789825, July 2025)”. This marks our fourth consecutive year of inclusion.

In the report, Perficient is included in the Technology Transformation Dominant category, where IDC defines participants as: “organizations [that] offer technology consulting advice and services as their primary service line to drive digital transformation.”

Built to Lead with Technology

IDC notes: “Many technology transformation dominant firms also offer business consulting services, and some are expanding their capabilities into design services and product engineering services as well.”

This aligns with our evolution as a consulting partner. At Perficient, we integrate strategy, implementation, and innovation, giving clients the technology foundations and AI readiness to accelerate transformation and achieve tangible outcomes.

An Industry in Motion

The report also highlights a key trend shaping the digital business consulting landscape:

“Acquisitions to boost AI/cloud/digital capabilities and to fill niche areas of expertise.”

Perficient’s strategic investments reflect this shift. We continue to deepen our AI-first capabilities, expand our industry expertise, and deliver consulting services grounded in execution.

Strategy to Execution, Powered by AI

Technology transformation at Perficient is built for scale, speed, and strategy. We help enterprises modernize platforms, streamline architectures, and align technology investments to real business outcomes. Our AI-powered Envision experience connects strategy to execution, combining proven frameworks with intelligent tools that help leaders prioritize, activate, and accelerate transformation. From capability mapping to platform selection, Envision turns insight into action and helps organizations move faster with confidence.

Ready to move from ambition to impact? Let’s define your AI-first strategy and build the foundation to lead what’s next.

Drupal 11’s AI Features: What They Actually Mean for Your Team

If you’ve been following the Drupal community lately, you’ve probably heard about the excitement with AI in Drupal 11 and the new Drupal AI Initiative. With over $100,000 in funding and 290+ AI modules already available, this will be a game changer.

But here’s the thing, AI in Drupal isn’t about replacing your team. It’s about making everyone more effective at what they already do best. Let’s talk through some of these new capabilities and what they mean for different teams in your organization.

Content Teams: Finally, An Assistant That Actually Helps

Creating quality content quickly has always been a challenge, but Drupal 11’s AI features tackle this head-on. The AI CKEditor integration gives content creators real-time assistance right in the editing interface, things like spelling corrections, translations, and contextual suggestions as you type.

The AI Content module is where things get interesting. It can automatically adjust your content’s tone for different audiences, summarize long content, and even suggest relevant taxonomy terms. For marketing teams juggling multiple campaigns, this means maintaining brand consistency without the usual back-and-forth reviews.

One feature that’s already saving teams hours is the AI Image Alt Text module. Instead of manually writing alt text for accessibility compliance, it generates descriptions automatically. The AI Translate feature is another game-changer for organizations with global reach—one-click multilingual content creation that actually understands context.

The bottom line? Your content team can focus on strategy and creativity instead of getting bogged down in routine tasks.

Developers: Natural Language Site Building

Here’s where Drupal 11 gets really exciting for a dev team. The AI Agents module introduces something we haven’t seen before, text-to-action capabilities. Developers can now modify Drupal configurations, create content types, and manage taxonomies just by describing what they need in spoken english.

Instead of clicking through admin interfaces, you can literally tell Drupal what you want, “Create a content type for product reviews with fields for rating, pros, cons, and reviewer information.” The system understands and executes these commands.

The AI module ecosystem supports over 21 major providers, OpenAI, Claude, AWS Bedrock, Google Vertex, and more. This means you’re not locked into any single AI provider and can choose the best model for specific tasks. The AI Explorer gives you a testing ground to experiment with prompts before pushing anything live.

For complex workflows, AI Automators let you chain multiple AI systems together. Think automated content transformation, field population, and business logic handling with minimal custom code.

The other great aspect of Drupal AI, is the open source backbone of Drupal, allows you to extend, add and build upon these agents in any way your dev team sees fit.

Marketing Teams: Data-Driven Campaign Planning

Marketing teams might be the biggest winners here. The AI Content Strategy module analyzes your existing content and provides recommendations for what to create next based on actual data, not guesswork. It identifies gaps in your content strategy and suggests targeted content based on audience behavior and industry trends.

The AI Search functionality means visitors can find content quickly, no more keyword guessing games. The integrated chatbot framework provides intelligent customer service that can access your site’s content to give accurate responses.

For SEO, the AI SEO module generates reports with user recommendations, reviewing content and metadata automatically. This reduces the need for separate SEO tools while giving insights right where you can act on them.

Why This Matters Right Now

The Drupal AI Initiative represents something more than just new features. With dedicated teams from leading agencies and serious funding behind it, this is Drupal positioning itself as the go-to platform for AI-powered content management.

For IT executives evaluating CMS options, Drupal 11’s approach is a great fit. You maintain complete control over your data and AI interactions while getting enterprise-grade governance with approval workflows and audit trails. It’s AI augmentation rather than AI replacement.

The practical benefits are clear: faster campaign launches, consistent brand voice across all content, and teams freed from manual tasks to focus on strategic work. In today’s competitive landscape, that kind of operational efficiency can make the difference between leading your market and playing catch-up.

The Reality Check

We all know, no technology is perfect. The success of these AI features, especially within the open source community, depends heavily on implementation and team adoption. You’ll need to spend time in training and process development to see real benefits. Like any new technology, there will be a learning curve as your team figures out the best ways to leverage these new features.

Based on what we are seeing within groups that have done early adoption of the AI features, they are seeing a good ROI on improvement of team efficiency, marketing time as well as reduced SEO churn.

If you’re considering how Drupal 11’s AI features might fit your organization, it’s worth having a conversation with an experienced implementation partner like Perficient. We can help you navigate the options and develop an AI strategy that makes sense for your specific situation.

Oracle’s flagship event is back—and it’s got a bold new name. What was once known as Oracle CloudWorld is now Oracle AI World, reflecting the seismic shift in enterprise technology: AI is no longer a buzzword, it’s the backbone of innovation.

From October 13–16, Oracle AI World will take over The Venetian Las Vegas with a packed agenda of keynotes, demos, and networking opportunities designed to help attendees harness the power of artificial intelligence across cloud infrastructure, applications, and data management.

Whether you’re exploring generative AI, building intelligent agents, or reimagining analytics, this event is your front-row seat to the future.

Meet us at our booth in AI World Hub in the Venetian to connect with subject matter experts and thought leaders and learn how we’ve leveraged our extensive expertise in Enterprise Resource Planning (ERP), Supply Chain Management, Human Capital Management, Enterprise Performance Management (EPM), Business Analytics, Oracle Cloud Infrastructure, and Oil and Gas to drive digital transformation for our customers.

Ask Us About Our Jumpstart Offers

Redwood Experience Jumpstart:

Our Redwood Experience Jumpstart is designed to accelerate your Redwood adoption via a series of collaborative sessions and assessments that introduce Redwood’s intuitive design and embedded AI capabilities, while aligning with your specific application needs and personalization goals.

Oracle AI Jumpstart:

Our Oracle AI Jumpstart is a structured engagement designed to help you quickly activate and scale Oracle’s embedded AI capabilities. Through a series of alignment sessions, demonstrations, and configuration activities, you’ll gain hands-on experience with Generative AI, machine learning, and prebuilt AI services that are seamlessly integrated into the Oracle Cloud Infrastructure and application ecosystem.

As an Oracle Partner with 25+ years of experience, we are committed to partnering with our clients to tackle complex business challenges and accelerate transformative growth. We’re excited to talk with attendees about how Perficient is helping clients unlock real value from Oracle’s AI-powered solutions—from Fusion Applications to OCI and beyond. Our team will be on-site, ready to share insights, answer questions, and explore how we can partner to drive smarter, faster decisions with Oracle AI.

Whether you’re attending Oracle AI World to learn, network, or just get inspired, make sure to carve out time to connect with Perficient to learn more about how we partner with our customers to forge the future. We’re here to help you turn AI ambition into action.

See you in Vegas!

Modern Healthcare has once again recognized Perficient among the largest healthcare management consulting firms in the U.S., ranking us ninth in its 2025 survey. This honor reflects not only our growth but also our commitment to helping healthcare leaders navigate complexity with clarity, precision, and purpose.

What’s Driving Demand: Innovation with Intent

As provider, payer, and MedTech organizations face mounting pressure to modernize, our work is increasingly focused on connecting digital investments to measurable business and health outcomes. The challenges are real—and so are the opportunities.

Healthcare leaders are engaging our experts to tackle shifts from digital experimentation to enterprise alignment in business-critical areas, including:

• Digital health transformation that eases access to care.
• AI and data analytics that accelerate insight, guide clinical decisions, and personalize consumer experiences.
• Workforce optimization that supports clinicians, streamlines operations, and restores time to focus on patients, members, brokers, and care teams.

These investments represent strategic maturity that reshapes how care is delivered, experienced, and sustained.

Operational Challenges: Strategy Meets Reality

Serving healthcare clients means working inside a system that resists simplicity. Our industry, technical, and change management experts help leaders address three persistent tensions:

1. Aligning digital strategy with enterprise goals. Innovation often lacks a shared compass. We translate divergent priorities—clinical, operational, financial—into unified programs that drive outcomes.
2. Controlling costs while preserving agility. Budgets are tight, but the need for speed and competitive relevancy remains. Our approach favors scalable roadmaps and solutions that deliver early wins and can flex as the health care marketplace and consumer expectations evolve.
3. Preparing the enterprise for AI. Many of our clients have discovered that their AI readiness lags behind ambition. We help build the data foundations, governance frameworks, and workforce capabilities needed to operationalize intelligent systems.

Related Insights: Explore the Digital Trends in Healthcare

Consumer Expectations: Access Is the New Loyalty

Our Access to Care research, based on insights from more than 1,000 U.S. healthcare consumers, reveals a fundamental shift: if your healthcare organization isn’t delivering a seamless, personalized, and convenient experience, consumers will go elsewhere. And they won’t always come back.

Many healthcare leaders still view competition as other hospitals or clinics in their region. But today’s consumer has more options—and they’re exercising them. From digital-first health experiences to hyper-local disruptors and retail-style health providers focused on accessibility and immediacy, the competitive field is rapidly expanding.

• Digital convenience is now a baseline. More than half of consumers who encountered friction while scheduling care went elsewhere.
• Caregivers are underserved. One in three respondents manage care for a loved one, yet most digital strategies treat the patient as a single user.
• Digital-first care is mainstream. 45% of respondents aged 18–64 have already used direct-to-consumer digital care, and 92% of those adopters believe the quality is equal or better to the care offered by their regular health care system.

These behaviors demand a rethinking of access, engagement, and loyalty. We help clients build experiences that are intuitive, inclusive, and aligned with how people actually live and seek care.

Looking Ahead: Complexity Accelerates

With intensified focus on modernization, data strategy, and responsible AI, healthcare leaders are asking harder questions. We’re helping them find and activate answers that deliver value now and build resilience for what’s next.

Our technology partnerships with Adobe, AWS, Microsoft, Salesforce, and other platform leaders allow us to move quickly, integrate deeply, and co-innovate with confidence. We bring cross-industry expertise from financial services, retail, and manufacturing—sectors where personalization and operational excellence are already table stakes. That perspective helps healthcare clients leapfrog legacy thinking and adopt proven strategies. And our fluency in HIPAA, HITRUST, and healthcare data governance ensures that our digital solutions are compliant, resilient, and future-ready.

Optimized, Agile Strategy and Outcomes for Health Insurers, Providers, and MedTech

Discover why we been trusted by the 10 largest U.S. health systems, 10 largest U.S. health insurers, and 14 of the 20 largest medical device firms. We are recognized in analyst reports and regularly awarded for our excellence in solution innovation, industry expertise, and being a great place to work.

Contact us to explore how we can help you forge a resilient, impactful future that delivers better experiences for patients, caregivers, and communities.

The move to SaaS is one of the biggest shifts happening in digital experience. It’s not just about technology, it’s about making platforms simpler, faster, and more adaptable to the pace of customer expectations.

Sitecore has leaned in with a clear vision: “It’s SaaS. It’s Simple. It’s Sitecore.”

Here are five reasons why more organizations are turning to Sitecore SaaS to power their digital experience strategies:

1. Simplicity: A Modern Foundation

Sitecore SaaS solutions like XM Cloud remove the burden of managing infrastructure and upgrades.

• No more complex version upgrades, updates happen automatically.
• Reduced reliance on IT for day-to-day maintenance.
• A leaner, more cost-effective foundation for marketing teams.

By simplifying operations, companies can focus on what matters most; delivering exceptional digital experiences.

2. Speed-to-Value: Launch Faster

Traditional DXPs can take months (or more) to implement and optimize. Sitecore SaaS is designed for speed:

• Faster deployments with prebuilt components.
• Seamless integrations with other SaaS and cloud tools.
• Empowerment for marketers to build and launch campaigns without heavy dev cycles.

Organizations adopting Sitecore SaaS are moving from planning to execution faster than ever.

3. Scalability: Grow Without Rebuilds

As customer expectations grow, so does the need to scale digital experiences quickly. Sitecore SaaS allows companies to:

• Spin up new sites, regions, or languages without starting from scratch.
• Adjust to spikes in demand without disruption.
• Add capabilities as the business evolves — without heavy upfront investment.

This scalability ensures brands can adapt as fast as their audiences do.

4. Continuous Innovation: Always Current

One of the most frustrating parts of traditional platforms is the upgrade cycle. Sitecore SaaS solves this with:

• Automatic access to the latest innovations — no disruptive “big bang” upgrades.
• Built-in adoption of emerging technologies like AI and machine learning.
• A platform that’s always modern, not years behind.

With Sitecore SaaS, companies get a future-proof DXP that evolves with them.

5. Composability Without the Complexity

Composable DXPs promise flexibility, but without the right foundation they can feel overwhelming. Sitecore SaaS makes composability practical:

• Start with XM Cloud as a core CMS foundation.
• Add personalization, commerce, or search when ready.
• Use APIs to integrate best-of-breed tools, without losing control.

This approach ensures organizations adopt what they need, when they need it without the complexity of managing multiple disconnected systems.

Why it Matters

Companies aren’t moving to Sitecore SaaS just to keep up with technology. They’re moving because it makes their organizations more agile, efficient, and competitive. SaaS with Sitecore means simpler operations, faster launches, continuous innovation, and a platform that grows alongside your business.

Azure Cosmos DB for NoSQL now supports hybrid search, it is a powerful feature that combines full-text search and vector search to deliver highly relevant and accurate results. This blog post provides a comprehensive guide for developers and architects to understand, implement, and leverage hybrid search capabilities in their applications.

• What is hybrid search?
• How hybrid search works in Cosmos DB
• Vector embedding
• Implementing hybrid search
  • Enable hybrid search.
  • Container set-up and indexing
  • Data Ingestion
  • Search Queries
• Code Example

What is Hybrid Search?

Hybrid search is an advanced search technology that combines keyword search (also known as full-text search) and vector search to deliver more accurate and relevant search results. It leverages the strengths of both approaches to overcome the limitations of each when used in isolation.

Key Components

• Full-Text Search: This traditional method matches the words you type in, using techniques like stemming, lemmatization, and fuzzy matching to find relevant documents. It excels at finding exact matches and is efficient for structured queries with specific terms. Employs the BM25 algorithm to evaluate and rank the relevance of records based on keyword matching and text relevance.
• Vector Search: This method uses machine learning models to represent queries and documents as numerical embeddings in a multidimensional space, allowing the system to find items with similar characteristics and relationships, even if the exact keywords don’t match. Vector search is particularly useful for finding information that’s conceptually similar to the search query.
• Reciprocal Rank Fusion (RRF): This algorithm merges the results from both keyword and vector search, creating a single, unified ranked list of documents. RRF ensures that relevant results from both search types are fairly represented.

Hybrid search is suitable for various use cases, such as:

• Retrieval Augmented Generation (RAG) with LLMs
• Knowledge management systems: Enabling employees to efficiently find pertinent information within an enterprise knowledge base.
• Content Management: Efficiently search through articles, blogs, and documents.
• AI-powered chatbots
• E-commerce platforms: Helping customers find products based on descriptions, reviews, and other text attributes.
• Streaming services: Helping users find content based on specific titles or themes.

Let’s understand vector search and full-text search before diving into hybrid search implementation.

Understanding of Vector Search

Vector search in Azure Cosmos DB for NoSQL is a powerful feature that allows you to find similar items based on their semantic meaning, rather than relying on exact matches of keywords or specific values. It is a fundamental component for building AI applications, semantic search, recommendation engines, and more.

Here’s how vector search works in Cosmos DB:

Vector embeddings

Vector embeddings are numerical representations of data in a high-dimensional space, capturing their semantic meaning. In this space, semantically similar items are represented by vectors that are closer to each other. The dimensionality of these vectors can be quite large. We have separate topics in this blog on how to generate vector embedding.

Storing and indexing vectors

Azure Cosmos DB allows you to store vector embeddings directly within your documents. You define a vector policy for your container to specify the vector data’s path, data type, and dimensions. Cosmos DB supports various vector index types to optimize search performance, accuracy, and cost:

• Flat: Provides exact k-nearest neighbor (KNN) search.
• Quantized Flat: Offers exact search on compressed vectors.
• DiskANN: Enables highly scalable and accurate Approximate Nearest Neighbor (ANN) search.

Querying

• Azure Cosmos DB provides the VectorDistance() system function, which can be used within SQL queries to perform vector similarity searches as part of vector search.

Understanding Full-Text Search

Azure Cosmos DB for NoSQL now offers full-text search functionality (feature is in preview at this time for certain Azure regions), allowing you to perform powerful and efficient text-based searches within your documents directly in the database. This significantly enhances your application’s search capabilities without the need for an external search service for basic full-text needs.

Indexing

To enable full-text search, you need to define a full-text policy specifying the paths for searching and add a full-text index to your container’s indexing policy. Without the index, full-text searches would perform a full scan. Indexing involves tokenization, stemming, and stop word removal, creating a data structure like an inverted index for fast retrieval. Multi-language support (beyond English) and stop word removal are in early preview.

Querying

Cosmos DB provides system functions for full-text search in the NoSQL query language. These include FullTextContains, FullTextContainsAll, and FullTextContainsAny for filtering in the WHERE clause. The FullTextScore function uses the BM25 algorithm to rank documents by their relevance.

How Hybrid Search works in Cosmos DB

• Data Storage: Your documents in Cosmos DB include both text fields (for full-text search) and vector embedding fields (for vector search).
• Indexing:
  • Full-Text Index: A full-text policy and index are configured on your text fields, enabling keyword-based searches.
  • Vector Index: A vector policy and index are configured on your vector embedding fields, allowing for efficient similarity searches based on semantic meaning.

• Querying: A single query request is used to initiate hybrid search, including both full-text and vector search parameters.
• Parallel Execution: The vector and full-text search components run in parallel.
  • VectorDistance() measures vector similarity.
  • FullTextContains() or similar functions find keyword matches, and `FullTextScore()` ranks results using BM25.

• Result Fusion: The RRF function merges the rankings from both searches (vector & full text), creating a combined, ordered list based on overall relevance.
• Enhanced Results: The final results are highly relevant, leveraging both semantic understanding and keyword precision.

Vector Embedding

Vector embedding refers to the process of transforming data (like text, images) into a series of numbers, or a vector, that captures its semantic meaning. In this n-dimensional space, similar data points are mapped closer together, allowing computers to understand and analyze relationships that would be difficult with raw data.

To support hybrid search in Azure Cosmos DB, enhance the data by generating vector embeddings from searchable text fields. Store these embeddings in dedicated vector fields alongside the original content to enable both semantic and keyword-based queries.

Steps to generate embeddings with Azure OpenAI models

Provision Azure OpenAI Resource

• Sign in to the Azure portal: Go to https://portal.azure.com and log in.
• Create a resource: Select “Create a resource” from the Azure dashboard and search for “Azure OpenAI”.

Deploy Embedding Model

• Navigate to your newly created Azure OpenAI resource and click on “Explore Azure AI Foundry portal” in the overview page.
• Go to the model catalog and search for embedding models.
• Select embedding model:
  • From the embedding model list, choose an embedding model like text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small.

• • 
  • Azure open AI embedding models https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings
• Deployment name: Provide a unique name for your deployment. This name is crucial for accessing the model via the API.
• Deploy the model: Click “Create” to deploy the embedding model.
• After deployment, the resource will appear under the ‘Deployments’ section.
  • 

Accessing and utilizing embeddings

• Endpoint and API Key: After deployment, navigate to your Azure OpenAI resource and find the “Keys and Endpoint” under “Resource Management”. Copy these values as they are needed for authenticating API calls.
• Integration with applications: Use the Azure OpenAI SDK or REST APIs in your applications, referencing the deployment name and the retrieved endpoint and API key to generate embeddings.

Code example for .NET Core

Note: Ensure you have the .NET Core 8 SDK installed

using Azure;
using Azure.AI.OpenAI;
using System;
using System.Linq;

namespace AzureOpenAIAmbeddings
{
    class Program
    {
        static async Task Main(string[] args)
        {
            // Set your Azure OpenAI endpoint and API key securely
            string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? "https://YOUR_RESOURCE_NAME.openai.azure.com/"; // Replace with OpenAI endpoint
            string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY") ?? "YOUR_API_KEY"; // Replace with OpenAI API key

            // Create an AzureOpenAIAClient
            var credentials = new AzureKeyCredential(apiKey);
            var openaiClient = new OpenAIClient(new Uri(endpoint), credentials);

            // Create embedding options
            EmbeddingOptions embeddingOptions = new()
            {
                DeploymentName = "text-embedding-ada-002", // Replace with your deployment name
                Input = { "Your text for generating embedding" },  // Text that require to generate embedding 
            };

            // Generate embeddings
            var returnValue = await openaiClient.GetEmbeddingsAsync(embeddingOptions);

            //Store generated embedding data to Cosmos DB along with your text content
            var embedding = returnValue.Value.Data[0].Embedding.ToArray()
        }
    }
}

Implementing Hybrid search

Implementing hybrid search in Azure Cosmos DB for NoSQL involves several key steps to combine the power of vector search and full-text search. This diagram illustrates the architecture of Hybrid Search in Azure Cosmos DB, leveraging Azure OpenAI for generating embedding, combining both vector-based and keyword-based search:

Step 1: Enable hybrid search in the Cosmos DB account

To implement hybrid search in Cosmos DB, begin by enabling both vector search and full-text search on the Azure Cosmos DB account.

• Navigate to Your Azure Cosmos DB for NoSQL Resource Page

• Access the Features Pane:

  • Select the “Features” pane under the “Settings” menu item.

• Enable Vector Search:

  • Locate and select the “Vector Search for NoSQL.” Read the description to understand the feature.
  • Click “Enable” to activate vector indexing and search capabilities.
  • 

• Enable Full-Text Search:

  • Locate and select the “Preview Feature for Full-Text Search” (Full-Text Search for NoSQL API (preview)). Read the description to confirm your intention to enable it.
  • Click “Enable” to activate full-text indexing and search capabilities.

                Notes:

• • • Once these features are enabled, they cannot be disabled.
    • Full Text Search (preview) may not be available in all regions at this time.

Step 2: Container Setup and Indexing

• Create a database and container or use an existing one.
  • Note: Adding a vector index policy to an existing container may not be supported. If so, you will need to create a new container.

• Define the Vector embedding policy on the container
  • You need to specify a vector embedding policy for the container during its creation. This policy defines how vectors are treated at the container level.
  • 

    {
       "vectorEmbeddings": [
           {
               "path":"/contentvector",
               "dataType":"float32",
               "distanceFunction":"cosine",
               "dimensions":1536
           },
    }
    

    • Path: Specify the JSON path to your vector embedding field (e.g., /contentvector).
    • Data type: Define the data type of the vector elements (e.g., float32).
    • Dimensions: Specify the dimensionality of your vectors (e.g., 1536 for text-embedding-ada-002).
    • Distance Function: Choose the distance metric for similarity calculation (e.g., cosine, dotProduct, or euclidean)

• Add Vector Index: Add a vector index to your container’s indexing policy. This enables efficient vector similarity searches.
  • 

• • • Path: Include the same vector path defined in your vector policy.
    • Type: Select the appropriate index type (flat, quantizedFlat, or diskANN).

• Define Full-Text Policy: Define a container-level full-text policy. This policy specifies which paths in your documents contain the text content that you want to search.
  • 

• • • Path: Specify the JSON path to your text search field
    • Language: content language

• Add Full-Text Index: Add a full-text index to the indexing policy, making full-text searches efficient
  • 

Hybrid search index (both Full-Text and Vector index)

{
  "indexingMode": "consistent",
  "automatic": true,
  "includedPaths": [
    {
      "path": "/*"
    }
  ],
  "excludedPaths": [
    {
      "path": "/_etag*/?"
    },
    {
      "path": "/contentvector/*"
    }
  ],
  "fullTextIndexes": [
    {
      "path": "/content"
    },
    {
      "path": "/description"
    }
  ],
  "vectorIndexes": [
    {
      "path": "/contentvector",
      "type": "diskANN"
    }
  ]
}

Exclude the Vector Path:

• To optimize performance during data ingestion, you must add the vector path to the “excludedPaths” section of your indexing policy. This prevents the vector path from being indexed by the default range indexes, which can increase RU charges and latency.

Step 3: Data Ingestion

• Generate Vector Embeddings: For every document, convert the text content (and potentially other data like images) into numerical vector embeddings using an embedding model (e.g., from Azure OpenAI Service). This topic is covered above.
• Populate Documents: Insert documents into your container. Each document should have:
  • The text content in the fields specified in your full-text policy (e.g., content, description).
  • The corresponding vector embedding in the field is specified in your vector policy (e.g., /contentvector).
  • Example document
  • 

Step 4: Search Queries

Hybrid search queries in Azure Cosmos DB for NoSQL combine the power of vector similarity search and full-text search within a single query using the Reciprocal Rank Fusion (RRF) function. This allows you to find documents that are both semantically similar and contain specific keywords.

SQL:  SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords))

VectorDistance(c. contentvector, @queryVector):

• VectorDistance(): This is a system function that calculates the similarity score between two vectors.
• @queryVector: This is a parameter representing the vector embedding of your search query. You would generate this vector embedding using the same embedding model used to create document vector embeddings.
• Return Value: Returns a similarity score based on the distance function defined in your vector policy (e.g., cosine, dot product, Euclidean).

FullTextScore(c.content, @searchKeywords):

• FullTextScore(): This is a system function that calculates a BM25 score, which evaluates the relevance of a document to a given set of search terms. This function relies on a full-text index on the specified path.
• @searchKeywords: This is a parameter representing the keywords or phrases you want to search for. You can provide multiple keywords separated by commas.
• Return Value: Returns a BM25 score, indicating the relevance of the document to the search terms. Higher scores mean greater relevance.

ORDER BY RANK RRF(…):

• RRF(…) (Reciprocal Rank Fusion): This is a system function that combines the ranked results from multiple scoring functions (like VectorDistance and FullTextScore) into a single, unified ranking. RRF ensures that documents that rank highly in either the vector search or the full-text search are prioritized in the final results.

Weighted hybrid search query:

SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords), [2, 1]).

• Optional Weights: You can optionally provide an array of weights as the last argument to RRF to control the relative importance of each component score. For example, to weight the vector search twice as important as the full-text search, you could use RRF(VectorDistance(c.contentvector, @queryVector), FullTextScore(c.content, @searchKeywords), [2,1]).

Multi-field hybrid search query:

SELECT TOP 10 * FROM c ORDER BY RANK RRF(VectorDistance(c.contentvector, @queryVector),VectorDistance(c.imagevector, @queryVector),

FullTextScore(c.content, @searchKeywords, FullTextScore(c.description, @searchKeywords,  [3,2,1,1]).

Code Example (.NET Core C#)

• Add Cosmos DB and OpenAI SDKs
• Get Cosmos DB connection string and create Cosmos DB client
• Get the OpenAI endpoint and key to create an OpenAI client
• Generate embedding for user query
• A hybrid search query to do a vector and keyword search

using Microsoft.Azure.Cosmos;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

namespace CosmosHybridSearch
{
    public class Product
    {
        public string Id { get; set; }
        public string Name { get; set; }
        public float[] DescriptionVector { get; set; } // Your vector embedding property
    }

    public class Program
    {
        private static readonly string EndpointUri = "YOUR_COSMOS_DB_ENDPOINT";
        private static readonly string PrimaryKey = "YOUR_COSMOS_DB_PRIMARY_KEY";
        private static readonly string DatabaseId = "YourDatabaseId";
        
        // Set your Azure OpenAI endpoint and API key securely.
        string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? "https://YOUR_RESOURCE_NAME.openai.azure.com/"; // Replace with your endpoint
        string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY") ?? "YOUR_API_KEY"; // Replace with your API key

        public static async Task Main(string[] args)
        {
            using CosmosClient client = new(EndpointUri, PrimaryKey);
            Database database = await client.CreateDatabaseIfNotExistsAsync(DatabaseId);
            Container container = database.GetContainer(ContainerId);

            // Create an AzureOpenAiEmbeddings instance - not online :)
            var credentials = new ApiKeyServiceClientCredentials(apiKey);
            AzureOpenAiEmbeddings openAiClient = new(endpoint, credentials);

            // Example: search your actual query vector and search term.
            float[] queryVector;
            string searchTerm = "lamp";

            EmbeddingOptions embeddingOptions = new()
            {
                DeploymentName = "text-embedding-ada-002", // Replace with your deployment name
                Input = searchTerm,
            };

            var queryVectorResponse = await openAICient.GetEmbeddingsAsync(embeddingOptions);
            queryVector = returnValue.Value.Data[0].Embedding.ToArray()

            // Define the hybrid search query using KQL
            QueryDefinition queryDefinition = new QueryDefinition(
              "SELECT top 10 * " +
              "FROM myindex " +
              "ORDER BY _vectorScore(desc, @queryVector), FullTextScore(_description, @searchTerm)")
           .WithParameter("@queryVector", queryVector)
           .WithParameter("@searchTerm", searchTerm);

           List<Product> products = new List<Product>();

           using FeedIterator<Product> feedIterator = container.GetItemQueryIterator<Product>(queryDefinition);

           while (feedIterator.HasMoreResults)
           {
              FeedResponse<Product> response = await feedIterator.ReadNextAsync();
              foreach (Product product in response)
              {
                  products.Add(product);
              }
           }

           // Process your search results
           foreach (Product product in products)
           {
              Console.WriteLine($"Product Id: {product.Id}, Name: {product.Name}");
           }
        }
    }
}

How to monitor hundreds of Key Vaults across multiple subscriptions for just $15-25/month

The Challenge: Key Vault Sprawl in Enterprise Azure

If you’re managing Azure at enterprise scale, you’ve likely encountered this scenario: Key Vaults scattered across dozens of subscriptions, hundreds of certificates and secrets with different expiry dates, and the constant fear of unexpected outages due to expired certificates. Manual monitoring simply doesn’t scale when you’re dealing with:

• Multiple Azure subscriptions (often 10-50+ in large organizations)
• Hundreds of Key Vaults across different teams and environments
• Thousands of certificates with varying renewal cycles
• Critical secrets that applications depend on
• Different time zones and rotation schedules

The traditional approach of spreadsheets, manual checks, or basic Azure Monitor alerts breaks down quickly. You need something that scales automatically, costs practically nothing, and provides real-time visibility across your entire Azure estate.

The Solution: Event-Driven Monitoring Architecture

Single Function App, Unlimited Key Vaults

Instead of deploying monitoring resources per Key Vault (expensive and complex), we use a centralized architecture:

Management Group (100+ Key Vaults)
           ↓
   Single Function App
           ↓
     Action Group
           ↓
    Notifications

This approach provides:

• Unlimited scalability: Monitor 1 or 1000+ Key Vaults with the same infrastructure
• Cross-subscription coverage: Works across your entire Azure estate
• Real-time alerts: Sub-5-minute notification delivery
• Cost optimization: $15-25/month total (not per Key Vault!)

How It Works: The Technical Deep Dive

1. Event Grid System Topics (The Sensors)

Azure Key Vault automatically generates events when certificates and secrets are about to expire. We create Event Grid System Topics for each Key Vault to capture these events:

Event Types Monitored:
• Microsoft.KeyVault.CertificateNearExpiry
• Microsoft.KeyVault.CertificateExpired  
• Microsoft.KeyVault.SecretNearExpiry
• Microsoft.KeyVault.SecretExpired

The beauty? These events are generated automatically by Azure – no polling, no manual checking, just real-time notifications when things are about to expire.

2. Centralized Processing (The Brain)

A single Azure Function App processes ALL events from across your organization:

// Simplified event processing flow
eventGridEvent → parseEvent() → extractMetadata() → 
formatAlert() → sendToActionGroup()

Example Alert Generated:
{
  severity: "Sev1",
  alertTitle: "Certificate Expired in Key Vault",
  description: "Certificate 'prod-ssl-cert' has expired in Key Vault 'prod-keyvault'",
  keyVaultName: "prod-keyvault",
  objectType: "Certificate",
  expiryDate: "2024-01-15T00:00:00.000Z"
}

3. Smart Notification Routing (The Messenger)

Azure Action Groups handle notification distribution with support for:

• Email notifications (unlimited recipients)
• SMS alerts for critical expiries
• Webhook integration with ITSM tools (ServiceNow, Jira, etc.)
• Voice calls for emergency situations.

Implementation: Infrastructure as Code

The entire solution is deployed using Terraform, making it repeatable and version-controlled. Here’s the high-level infrastructure:

Resource Architecture

# Single monitoring resource group
resource "azurerm_resource_group" "monitoring" {
  name     = "rg-kv-monitoring-${var.timestamp}"
  location = var.primary_location
}

# Function App (handles ALL Key Vaults)
resource "azurerm_linux_function_app" "kv_processor" {
  name                = "func-kv-monitoring-${var.timestamp}"
  service_plan_id     = azurerm_service_plan.function_plan.id
  # ... configuration
}

# Event Grid System Topics (one per Key Vault)
resource "azurerm_eventgrid_system_topic" "key_vault" {
  for_each = { for kv in var.key_vaults : kv.name => kv }
  
  name                   = "evgt-${each.key}"
  source_arm_resource_id = "/subscriptions/${each.value.subscriptionId}/resourceGroups/${each.value.resourceGroup}/providers/Microsoft.KeyVault/vaults/${each.key}"
  topic_type            = "Microsoft.KeyVault.vaults"
}

# Event Subscriptions (route events to Function App)
resource "azurerm_eventgrid_event_subscription" "certificate_expiry" {
  for_each = { for kv in var.key_vaults : kv.name => kv }
  
  azure_function_endpoint {
    function_id = "${azurerm_linux_function_app.kv_processor.id}/functions/EventGridTrigger"
  }
  
  included_event_types = [
    "Microsoft.KeyVault.CertificateNearExpiry",
    "Microsoft.KeyVault.CertificateExpired"
  ]
}

CI/CD Pipeline Integration

The solution includes an Azure DevOps pipeline that:

1. Discovers Key Vaults across your management group automatically
2. Generates Terraform variables with all discovered Key Vaults
3. Deploys infrastructure using infrastructure as code
4. Validates deployment to ensure everything works

# Simplified pipeline flow
stages:
  - stage: DiscoverKeyVaults
    # Scan management group for all Key Vaults
    
  - stage: DeployMonitoring  
    # Deploy Function App and Event Grid subscriptions
    
  - stage: ValidateDeployment
    # Ensure monitoring is working correctly

Cost Analysis: Why This Approach Wins

Traditional Approach (Per-Key Vault Monitoring)

100 Key Vaults × $20/month per KV = $2,000/month
Annual cost: $24,000

This Approach (Centralized Monitoring)

Base infrastructure: $15-25/month
Event Grid events: $2-5/month  
Total: $17-30/month
Annual cost: $204-360

Savings: 98%+ reduction in monitoring costs

Detailed Cost Breakdown

Implementation Guide: Getting Started

Prerequisites

1. Azure Management Group with Key Vaults to monitor
2. Service Principal with appropriate permissions:
   • Reader on Management Group
   • Contributor on monitoring subscription
   • Event Grid Contributor on Key Vault subscriptions
3. Azure DevOps or similar CI/CD platform

Step 1: Repository Setup

Create this folder structure:

keyvault-monitoring/
├── terraform/
│   ├── main.tf              # Infrastructure definitions
│   ├── variables.tf         # Configuration variables
│   ├── terraform.tfvars     # Your specific settings
│   └── function_code/       # Function App source code
├── azure-pipelines.yml      # CI/CD pipeline
└── docs/                    # Documentation

Step 2: Configuration

Update terraform.tfvars with your settings:

# Required configuration
notification_emails = [
  "your-team@company.com",
  "security@company.com"
]

primary_location = "East US"
log_retention_days = 90

# Optional: SMS for critical alerts
sms_notifications = [
  {
    country_code = "1"
    phone_number = "5551234567"
  }
]

# Optional: Webhook integration
webhook_url = "https://your-itsm-tool.com/api/alerts"

Step 3: Deployment

The pipeline automatically:

1. Scans your management group for all Key Vaults
2. Generates infrastructure code with discovered Key Vaults
3. Deploys monitoring resources using Terraform
4. Validates functionality with test events

Expected deployment time: 5-10 minutes

Step 4: Validation

Test the setup by creating a short-lived certificate:

# Create test certificate with 1-day expiry
az keyvault certificate create \
  --vault-name "your-test-keyvault" \
  --name "test-monitoring-cert" \
  --policy '{
    "issuerParameters": {"name": "Self"},
    "x509CertificateProperties": {
      "validityInMonths": 1,
      "subject": "CN=test-monitoring"
    }
  }'

# You should receive an alert within 5 minutes

Operational Excellence

Monitoring the Monitor

The solution includes comprehensive observability:

// Function App performance dashboard
FunctionAppLogs
| where TimeGenerated > ago(24h)
| summarize 
    ExecutionCount = count(),
    SuccessRate = (countif(Level != "Error") * 100.0) / count(),
    AvgDurationMs = avg(DurationMs)
| extend PerformanceScore = case(
    SuccessRate >= 99.5, "Excellent",
    SuccessRate >= 99.0, "Good", 
    "Needs Attention"
)

Advanced Features and Customizations

1. Integration with ITSM Tools

The webhook capability enables integration with enterprise tools:

// ServiceNow integration example
const serviceNowPayload = {
  short_description: `${objectType} '${objectName}' expiring in Key Vault '${keyVaultName}'`,
  urgency: severity === 'Sev1' ? '1' : '3',
  category: 'Security',
  subcategory: 'Certificate Management',
  caller_id: 'keyvault-monitoring-system'
};

2. Custom Alert Routing

Different Key Vaults can route to different teams:

// Route alerts based on Key Vault naming convention
const getNotificationGroup = (keyVaultName) => {
  if (keyVaultName.includes('prod-')) return 'production-team';
  if (keyVaultName.includes('dev-')) return 'development-team';
  return 'platform-team';
};

3. Business Hours Filtering

Critical alerts can bypass business hours, while informational alerts respect working hours:

const shouldSendImmediately = (severity, currentTime) => {
  if (severity === 'Sev1') return true; // Always send critical alerts
  
  const businessHours = isBusinessHours(currentTime);
  return businessHours || isNearBusinessHours(currentTime, 2); // 2 hours before business hours
};

Troubleshooting Common Issues

Issue: No Alerts Received

Symptoms:

Events are visible in Azure, but no notifications are arriving

Resolution Steps:

1. Check the Action Group configuration in the Azure Portal
2. Verify the Function App is running and healthy
3. Review Function App logs for processing errors
4. Validate Event Grid subscription is active

Issue: High Alert Volume

Symptoms:

Too many notifications, alert fatigue

Resolution:

// Implement intelligent batching
const batchAlerts = (alerts, timeWindow = '15m') => {
  return alerts.reduce((batches, alert) => {
    const key = `${alert.keyVaultName}-${alert.objectType}`;
    batches[key] = batches[key] || [];
    batches[key].push(alert);
    return batches;
  }, {});
};

Issue: Missing Key Vaults

Symptoms: Some Key Vaults are not included in monitoring

Resolution:

1. Re-run the discovery pipeline to pick up new Key Vaults
2. Verify service principal has Reader access to all subscriptions
3. Check for Key Vaults in subscriptions outside the management group

Introduction

With over 9+ years of experience in mobile development and a strong focus on React Native, I’ve always been eager to stay ahead of the curve. Recently, I’ve been exploring the future of React Native, diving into upcoming features, AI integrations, and Meta’s long-term vision for cross-platform innovation. React Native has been a game-changing framework in the mobile space, empowering teams to build seamless cross-platform applications using JavaScript and React. Backed by Meta (formerly Facebook), it continues to evolve rapidly, introducing powerful new capabilities, optimizing performance, and increasingly integrating AI-driven solutions.

In this article, we’ll explore upcoming React Native features, how AI is integrating into the ecosystem, and Meta’s long-term vision for cross-platform innovation.

Upcoming Features in React Native

React Native’s core team, alongside open-source contributors, is actively working on several exciting updates. Here’s what’s on the horizon:

Fabric: The New Rendering Engine

Fabric modernizes React Native’s rendering infrastructure to make it faster, more predictable, and easier to debug.
Key benefits:

• Concurrent React support
• Synchronous layout and rendering
• Enhanced native interoperability

As of 2025, Fabric is being gradually enabled by default in newer React Native versions (0.75+).

TurboModules

A redesigned native module system aimed at improving startup time and memory usage. TurboModules allow React Native to lazily load native modules only when needed, reducing app initialization overhead.

Hermes 2.x and Beyond

Meta’s lightweight JavaScript engine for React Native apps continues to get faster, with better memory management and debugging tools like Chrome DevTools integration.

New improvements:

• Smaller bundle sizes
• Better GC performance
• Faster cold starts

React Native Codegen

A system that automates native bridge generation, making native module creation safer and faster, while reducing runtime errors. This is essential for scaling large apps with native modules.

AI Integrations in React Native

Artificial Intelligence is not just for backend systems or web apps anymore. AI is actively being integrated into React Native workflows, both at runtime and during development.

Where AI is showing up in React Native:

• AI-Powered Code Suggestions & Debugging
  Tools like GitHub Copilot, ChatGPT, and AI-enhanced IDE extensions are streamlining development, providing real-time code fixes, explanations, and best practices.

• ML Models in React Native Apps
  With frameworks like TensorFlow.js, ML Kit, and custom CoreML/MLModel integration via native modules, developers can embed models for:
  • Image recognition
  • Voice processing
  • Predictive text
  • Sentiment analysis

• AI-Based Performance Monitoring & Crash Prediction
  Meta and third-party analytics tools are embedding AI to predict crashes and performance bottlenecks, offering insights before problems escalate in production apps.

• AI-Driven Accessibility Improvements
  Automatically generating image descriptions or accessibility labels using computer vision models is becoming a practical AI use case in mobile apps.

Meta’s Vision for Cross-Platform Innovation

Meta’s vision for React Native is clear: to make cross-platform development seamless, high-performing, and future-proof.

What Meta is focusing on:

• Unified Rendering Pipeline (Fabric)
• Tight integration with Concurrent React
• Deep AI integrations for personalization, recommendations, and moderation
• Optimized developer tooling (Flipper, Hermes, Codegen)
• Expanding React Native’s use across Meta’s product family (Facebook, Instagram, Oculus apps)

Long-Term:

Expect more AI-powered tooling, better integration between React (Web) and React Native, and Meta investing in AI-assisted developer workflows.

Conclusion

React Native’s future is bright, with Fabric, TurboModules, Hermes, and AI integrations reshaping how mobile apps are built and optimized. Meta’s continuous investment ensures that React Native remains not only relevant but also innovative in the ever-changing app development landscape.

As AI becomes a core part of both our development tools and end-user experiences, React Native developers are uniquely positioned to lead the next generation of intelligent, performant, cross-platform apps.