As the year wraps up, so does an incredible run of conferences spotlighting the best in Microsoft 365, Power Platform, and Copilot innovation. We’re thrilled to share that Amarender Peddamalku, Microsoft MVP and Practice Lead for Microsoft Modern Work at Perficient, will be speaking at the Microsoft 365, Power Platform & Copilot Conference in Dallas, November 3–7.

Amarender has been a featured speaker at every TechCon365, DataCon, and PWRCon event this year—and Dallas marks the final stop on this year’s tour. If you’ve missed him before, now’s your chance to catch his insights live!

With over 15 years of experience in Microsoft technologies and a deep focus on Power Platform, SharePoint, and employee experience, Amarender brings practical, hands-on expertise to every session. Here’s where you can find him in Dallas:

Workshops & Sessions

• Power Automate Bootcamp: From Basics to Brilliance
  Mon, Nov 3 | 9:00 AM – 5:00 PM | Room G6
  A full-day, hands-on workshop for Power Automate beginners.

• Power Automate Multi-Stage Approval Workflows
  Tue, Nov 4 | 9:00 AM – 5:00 PM | Room G2
  Wed, Nov 5 | 3:50 PM – 5:00 PM | Room G6
  Learn how to build dynamic, enterprise-ready approval workflows.

• Ask the Experts
  Wed, Nov 5 | 12:50 PM – 2:00 PM | Expo Hall
  Bring your questions and get real-time answers from Amarender and other experts.

• Build External-Facing Websites Using Power Pages
  Thu, Nov 6 | 1:00 PM – 2:10 PM | Room D
  Discover how to create secure, low-code websites with Power Pages.

• Automate Content Processing Using AI & SharePoint Premium
  Thu, Nov 6 | 4:20 PM – 5:30 PM | Room G6
  Explore how AI and SharePoint Premium (formerly Syntex) can transform content into knowledge.

Whether you’re just getting started with Power Platform or looking to scale your automation strategy, Amarender’s sessions will leave you inspired and equipped to take action.

Register now!

Datadog Synthetic Monitoring provides automated, simulated user journeys to proactively confirm the health and performance of websites and APIs, helping detect issues before users experience them. Integrating this into our Azure DevOps pipeline ensures that only builds where core site functionality is verified get promoted, reducing the risk of regressions in production. This approach is especially valuable in Sitecore projects, where critical web journeys and API endpoints are essential to user experience.

Why Use This Approach?

• Immediate feedback: Failing releases are blocked before merging, saving post-release firefighting.
• Coverage: Synthetic tests simulate real browser actions and API calls over real user flows.
• Reliability: Automated testing delivers consistent, repeatable validation without manual steps.
• Visibility: Results are unified within both Datadog and Azure DevOps for full traceability.
• Scalability: As Sitecore projects grow, synthetic tests can be expanded to cover new endpoints and user scenarios without significant pipeline changes.
• Environment parity: Tests can be run against staging, UAT, or pre-production environments before the live rollouts for safer releases.

Prerequisites

• Active Datadog account with Synthetic Monitoring enabled.
• Datadog API and Application keys created with the appropriate access scope.
• Azure DevOps project with a working YAML-based CI/CD pipeline.
• Secure variable storage in Azure DevOps (e.g., Variable Groups, Secret Variables) for credentials.
• Stable and accessible endpoint URLs for Sitecore environment(s) under test.

High-Level Integration Process

1. Datadog Synthetic Test Creation

• Create Browser and/or HTTP Synthetic Tests in Datadog tailored for key Sitecore application flows, such as:
  • Homepage load and rendering
  • Login flow and user dashboard navigation
  • Core API calls (search, content retrieval)
  • Critical commerce or form submissions
• Use relevant tags (e.g., premerge) for search/query filtering by the CI pipeline.
• Configure assertions to confirm critical elements:
  • Content correctness
  • HTTP status codes
  • Redirect targets
  • Response time SLAs

• Validate tests in Datadog’s UI with multiple runs before pipeline integration.

2. Azure DevOps Pipeline Configuration

The Azure DevOps YAML pipeline is set up to invoke Datadog CI, run all tests matching our tag criteria, and fail the pipeline if any test fails.

Key Pipeline Steps

• Install Datadog CI binary: Downloads and installs the CLI in the build agent.
• Run Synthetic Tests: Uses the environment variables and search tags to pick synthetic tests (e.g., all with type: browser tag: remerge) and runs them directly.
• JUnit Reporting & Artifacts: The CLI output is saved, and a JUnit-formatted result file is generated for Azure DevOps’ Tests UI. All test outputs are attached as build artifacts.
• Conditional Fast-forward Merge: The pipeline proceeds to a gated merge to release/production only if all synthetics pass.

How Results and Flow Work

When All Tests Pass

• The pipeline completes the Premerge_Datadog_Synthetics stage successfully.
• Test summaries (JUnit) and CLI outputs are attached to the pipeline run.
• Approval-gated merge to the Release branch is unblocked; approvers can verify test results before promotion.

Build artifacts include full logs for further review.

When Any Test Fails

• If any synthetic (browser/API) test fails, the CLI exits with a non-zero exit code.
• The JUnit summary will contain failure info and a link to the log details.
• The pipeline stage fails (Premerge_Datadog_Synthetics), halting the fast-forward merge.
• Approvers can review the failure in test results and attached artifacts within Azure DevOps.

Only successful resolution and green reruns allow code promotion.

Best Practices for Datadog Synthetic Monitoring

• Run tests in parallel to reduce wait times.
• Use separate synthetic tests per microservice or major Sitecore area to isolate failures.
• Monitor test trends in Datadog to detect gradual performance regression over time.
• Limit sensitive data in synthetic flows by avoiding the storage of actual credentials.
• Schedule periodic synthetic runs outside CI/CD to catch environment fluctuations unrelated to code changes.

Security Considerations

• Store Datadog keys as secret variables in Azure DevOps.
• Restrict permission for synthetic management to trusted CICD admins.
• Avoid embedding credentials or sensitive payloads in test scripts.

Conclusion

By integrating Datadog Synthetic Monitoring directly into our CI/CD pipeline with Azure DevOps. Sitecore teams gain a safety net that blocks faulty builds before they hit production, while keeping a detailed audit trail. Combined with careful test design, secure key management, and continuous expansion of coverage, this approach becomes a cornerstone of proactive web application quality assurance.

Microsoft Ignite 2025 is right around the corner—and Perficient is showing up with purpose and a plan to help you unlock real results with AI.

As a proud member of Microsoft’s Inner Circle for AI Business Solutions, we’re at the forefront of helping organizations accelerate their AI transformation. Whether you’re exploring custom copilots, modernizing your data estate, or building secure, responsible AI solutions, our team is ready to meet you where you are—and help you get where you want to go.

Here’s where you can connect with us during Ignite:

Join Us for Happy Hour
Unwind and connect with peers, Microsoft leaders, and the Perficient team at our exclusive happy hour just steps from the Moscone Center.
Fogo de Chão | November 17 | 6:00–9:00 PM
RSVP Now

Book a Strategy Session
Need a quiet space to talk AI strategy? We’ve secured a private meeting space across from the venue—perfect for 1:1 conversations about your AI roadmap.
Ember Lounge — 201 3rd St, 8th floor, Suite 8016 | November 18-20
Reserve Your Time

From copilots to cloud modernization, we’re helping clients across industries turn AI potential into measurable impact. Let’s connect at Ignite and explore what’s possible.

Microsoft Copilot Studio is a low-code/no-code workspace from Microsoft that helps you build, customize, and manage AI-powered assistants (called copilots or agents) for your organization. Think of it as a visual toolkit that connects large language models (LLMs) to your data sources (SharePoint, OneDrive, Dataverse, etc.), custom logic, and UI behaviors, enabling you to create helpful assistants that understand your company context and answer real workplace questions. Why it matters (in plain layman terms) Copilot Studio lets non-AI experts create assistants that do real work — summarize documents, answer policy questions, extract data, or route requests — without writing massive amounts of code. For beginners, it’s a gateway to real-world AI: you don’t need to manage models or infra; you focus on prompts, connectors, and experience.

Key Uses and Benefits of Copilot Studio

• Create knowledge assistants that read your SharePoint/OneDrive files and answer employee questions.
• Build task-oriented agents that can schedule meetings, draft emails, or generate reports.
• Customize tone and behavior (persona) so the Copilot matches your team’s voice.
• Control security and data access through Microsoft 365 connectors and policies.
• Rapidly prototype and test in a playground, then publish to users.

A Beginner-Friendly Guide to Creating Your First Agent in Microsoft Copilot Studio

Prerequisites

• A Microsoft 365 account with admin/user access where Copilot is enabled.
• Appropriate licensing that includes Copilot or Copilot Studio access.
• Basic familiarity with SharePoint/OneDrive if you want to connect document data.
• Browser access to Copilot Studio (works best in Chrome/Edge).

Step 1 — Open Copilot Studio

1. Go to the Copilot Studio URL (copilot.microsoft.com) provided by Microsoft for your tenant, or sign into Microsoft 365 and navigate to Copilot Studio from the apps list.
2. If it’s your first time, the studio may prompt permission; approve the necessary consent for your account.

Step 2 — Start a New Agent (Copilot)

1. Click “Create” or “New Copilot/Agent”.
2. Enter a name (e.g., “Sales Helper”) and a short description of what it should do.
3. Optionally upload an icon or image to help users recognize it.

Step 3 — Define the Copilot’s Persona and Behavior

1. Choose a persona/tone: professional, friendly, concise, etc.
2. Create a few example prompts or instruction templates that guide how the Copilot responds (e.g., “Summarize this document into 3 bullet points focused on action items.”).
3. Set limits for response length and determine whether to ask clarifying questions.

Step 4 — Connect Data Sources (Skills/Connectors)

1. Click “Add Connector” or “Add Skill”.
2. Choose from built-in connectors like SharePoint, OneDrive, Teams, Dataverse, or custom connectors.
3. Authenticate the connector and select which sites/folders or tables the agent can access.
4. Optionally configure indexing or metadata settings to enable the Copilot to find relevant content quickly.

Step 5 — Add Actions and Tools (Optional)

1. Add any task-specific tools, such as calendar access, email drafting tools, or approvals.
2. Map triggers (for example, if the user asks “create meeting,” the Copilot can open a meeting draft).
3. If you have developer resources, you can add custom actions via low-code flows (Power Automate) or APIs.

Step 6 — Test in the Playground

1. Use the built-in test/playground area to ask sample questions.
2. Try different prompts and tweak persona or data scope based on the results.
3. Check for hallucinations or wrong data access; adjust the connector or prompt controls.

Step 7 — Set Security and Governance

1. Configure user access to determine who can use and edit this Copilot.
2. Define data usage settings and retention as per your org policies.
3. Enable logging and monitoring to audit queries and outputs.

Step 8 — Publish and Share

1. When satisfied, click “Publish” or “Deploy.”
2. Share the agent with users or teams (via Teams, SharePoint, or a link).
3. Collect user feedback and iterate—improvements are usually quick and ongoing.

Simple Prompt Examples to Get Useful Answers

• “Summarize the attached policy into three key action points for frontline staff.”
• “Find mentions of ‘budget’ in these documents and list the related amounts and dates.”
• “Draft a 150-word email to schedule a follow-up meeting next Wednesday.”

Common Beginner Mistakes and Tips

• Too broad data access: limit connectors to only the sites/folders needed.
• Vague prompts: provide examples and templates to help the assistant understand the expected format.
• Skipping governance: always set clear permissions and logging for sensitive data.
• Expect iterative improvement: start small, test often, and update prompts and connectors.

Conclusion

Microsoft Copilot Studio makes creating AI assistants approachable even if you’re new to AI, and it combines model power with your company data, connectors, and low-code tooling so you can craft copilots that actually solve workplace problems. Start with a small, focused agent (one data source, one use case), test it in the playground, tighten security, and expand from there. With the official docs and community articles as guides, you’ll be iterating and improving helpful assistants in no time.

Further Learning Links

• Microsoft Copilot Studio documentation
• Copilot Studio overview
• What is Artificial Intelligence (AI)? / Blogs / Perficient

If you’d told me twenty years ago that I’d be working in the technology industry, I would have laughed. My background? Art, history, and social services—not exactly the usual path to tech. I still sew and do needlework, and I miss the days when you had to check an answering machine or hunt down a pay phone. Honestly, I thrived without knowing what my second cousin, three times removed, had for breakfast this morning. Sometimes, I feel like the old guy on the corner yelling, “get off my lawn!”

But life has a way of surprising us. My career took a hard left turn and landed me in the world of technology. At first, I thought, “No problem. My skills—problem solving, analysis, and communication—are still useful.” I didn’t need to dive too deep into the tech pool; basic Excel and PowerPoint were enough. Fourteen years later, I’m proof that a non-technical person can survive (and even thrive) in a technology company.

Then came 2025. Our new CEO announced that we were going to be an AI-First company. We were not only encouraged, but expected to use AI in our day-to-day work. Cue the mini panic attack. My mind raced: What part of my job could I hand over to AI? How was I supposed to figure this out?

The lowest hanging fruit was my semi-annual reports. But I actually enjoy making those! Me and my trusty adding machine (yes, I still use it) making charts and graphs, discovering trends, and recapping the year—it’s something I look forward to. I didn’t want to give that away to my computer.

But change was coming, whether I liked it or not. So, I took a deep breath and got on board. I completed every training offered and sat down with a coworker who was genuinely excited about AI. Together, we made a plan and found ways to integrate AI into my workflow. Anytime someone said, “You can use Copilot for that,” I’d ask how. Gradually, my panic eased and I started to see the possibilities.

Now, I use Copilot to summarize long technical documents in plain language, generate images for presentations, and organize meeting notes. I’m still not ready to give up my trusty adding machine or hand over my reports, but I’m finding other ways to use these tools to make my life easier.

Most people see AI as something that gives them more time in their day. I used to think it would take away the parts of my job I enjoyed. Turns out, I was mistaken. If you’re like me—afraid of adopting new technology and unsure how it can help—take a deep breath and start small. Try it out and see if it really does save you time. And if you haven’t asked Copilot to rewrite an email as if it were from a pirate yet, you’re missing out! Not every use case works for everyone, but everyone can find a use case.

So here’s my advice:
You don’t have to be an expert to benefit from AI. Start with one small task, ask questions, and let curiosity lead the way. You might be surprised at how much easier your work becomes—and you might even have a little fun along the way.

PS… I was able to use this post as a learning opportunity.  I asked Copilot to review my article and make suggestions – most of which made the final draft.  Another small step.

Perficient is proud to be included in the IDC ServiceScape: Worldwide and U.S. Microsoft Power Apps Low-Code/No-Code Custom Application Development Services, 2025 (Doc# US53748825 September 2025) report. We believe this inclusion highlights our commitment to helping enterprises accelerate innovation and streamline development through Microsoft Power Platform.

This IDC ServiceScape offers a comprehensive guide on the key capabilities of custom application development service providers using the Microsoft Power Apps low-code/no-code development platform, featuring services from companies including Perficient. The status of each service capability is categorized as fully supported, partially supported, partner provided, road map, or not supported, aiding services buyers in quickly identifying which vendors align with their changing requirements.

Powering Innovation with Microsoft Power Platform

As digital transformation accelerates, low-code/no-code platforms are becoming essential tools for agility and innovation. Perficient is proud to be included in this evolving landscape and remains committed to delivering solutions that drive real business outcomes.

“Our approach to Power Platform is rooted in strategy, scale, and speed. We’re not just building apps—we’re enabling transformation. By combining governance frameworks, multi-shore delivery, and AI-powered experiences, we help clients unlock the full potential of low-code development and drive meaningful business outcomes.” – Eric Schmitt, Director of Microsoft Business Applications.

Perficient offers a comprehensive suite of low-code/no-code services designed to accelerate transformation:

• App modernization programs to migrate legacy systems to Power Platform
• Intelligent automation and rapid RPA migration (e.g., UiPath to Power Automate Desktop)
• Custom Copilot envisioning workshops and enterprise app development
• Governance engagements including CoE setup and citizen developer enablement
• Process mining and lifecycle management
• Multi-shore delivery models with agile development pods and managed services

We’re also increasing investment in Copilot Studio Agents, helping clients build custom functionality and deploy agents within Power Platform environments. Our governance frameworks ensure scalable, secure adoption—whether you’re enabling citizen developers or launching enterprise-wide automation programs.

Learn more about our Power Platform capabilities: Power Platform / Perficient

Perficient is a global digital consultancy with over 7,000 colleagues worldwide, operating as one unified team across North America, LATAM, and India. With deep expertise in industries like Healthcare & Life Sciences, Manufacturing, and Automotive, we deliver strategic technology solutions that drive measurable outcomes. Our Microsoft practice is backed by more than 25 years of experience and over 250 certified cloud consultants, with strong capabilities in Azure, M365, Dynamics CRM, and Power Platform.

Perficient differentiates through global delivery, scalability, and robust governance. With 95% of our business coming from repeat clients, we’re proud to be a trusted partner in building AI-first, low-code solutions that deliver real business value.

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

We’re proud to share that Perficient has been named to the 2025–2026 Microsoft AI Business Solutions Inner Circle—an elite group of strategic partners recognized for delivering transformative, AI-powered solutions that drive measurable business outcomes.

This recognition places Perficient among the top echelon of Microsoft’s global partner ecosystem, reflecting our deep expertise in Microsoft technologies and our commitment to helping clients embrace AI-first transformation. Through our AI Business Solutions practice, we empower organizations to boost productivity, accelerate decision-making, and improve business agility by embedding generative AI and Copilot experiences into everyday workflows.

Microsoft’s strategic focus in FY26 centers on enabling organizations to become Frontier Firms—those that operate with agility, scale rapidly, and generate value faster by leveraging hybrid teams of humans and AI agents. These firms are structured around on-demand intelligence and are reshaping how work gets done. Microsoft defines Frontier Firms as those that:

• Enrich employee experiences
• Reinvent customer engagement
• Reshape business processes
• Bend the curve on innovation

Perficient is proud to partner with Microsoft in helping our clients become Frontier Firms. By leveraging technologies like Microsoft 365 Copilot, Dynamics 365 Copilot, Power Platform, and Copilot Studio, we deliver integrated solutions that modernize operations and elevate the employee experience—unlocking new levels of efficiency, insight, and innovation.

Inclusion in the Inner Circle also provides Perficient with privileged access to Microsoft leadership, strategic insights, and early adoption opportunities. This access enables us to stay ahead of the curve and deliver future-ready solutions tailored to our clients’ unique needs.

Read more about this exciting recognition in our official press release.

Microsoft Copilot is an AI-powered assistant embedded across the Microsoft 365 ecosystem, designed to enhance productivity, streamline workflows, and empower users with intelligent automation. Built on Large Language Models (LLMs) like GPT-4 and GPT-5, and tightly integrated with Microsoft Graph, Copilot transforms how professionals interact with tools like Word, Excel, PowerPoint, Outlook, and Teams.

Microsoft Copilot is an LLM-powered AI assistant by Microsoft, similar to OpenAI’s ChatGPT. Under the Copilot brand, Microsoft has released a variety of products. Here’s a timeline of key releases:

Core Features of Microsoft Copilot

Copilot offers a wide range of capabilities. Specifically, it supports:

• Data Analysis: Analyzes large datasets, identifies trends, and generates insights.
• Document Creation: Drafts reports, emails, and presentations using natural language prompts.
• Project Management: Tracks tasks, schedules meetings, and summarizes conversations.
• Workflow Automation: Automates repetitive tasks like data entry and report generation.
• Communication: Summarizes emails, drafts responses, and manages inboxes.
• Security & Privacy: Honors Conditional Access, MFA, and data boundaries.

These features span across multiple Microsoft 365 apps, making Copilot a versatile productivity tool.

How Microsoft Copilot Works as an AI tool

At its core, Copilot combines:

• Large Language Models (LLMs) from OpenAI
  • It orchestrates large language models (like GPT-4 /GPT-5 via Azure OpenAI) to understand, generate, and summarize content in context.
• Microsoft Graph for contextual enterprise data
  • Copilot uses Microsoft Graph and semantic indexing—adding metadata and vector embeddings to content—enhancing intelligent retrieval when generating responses.
• Natural Language Processing (NLP) for understanding user intent
• Uses plain language prompts to perform complex tasks
• Microsoft 365 APIs for secure integration

 Microsoft Copilot Workflow

Security & Compliance

• Data Access: Copilot only accesses data that the user is authorized to view.
• Encryption: All data is encrypted in transit.
• MFA & Conditional Access: Fully supported for enterprise-grade security.

 Advantages of Microsoft Copilot

Copilot delivers several benefits:

• Automation: Reduces manual tasks like writing and formatting.
• Data Insights: Analyzes trends and creates visualizations.
• Contextual Intelligence: Uses enterprise data for tailored responses.
• Time Savings: Speeds up routine work and decision-making.
• Security: Honors user permissions and governance policies.

As a result, organizations can achieve measurable productivity gains.

Day-to-Day Uses in the Software Industry

For Developers

• Code Suggestions: GitHub Copilot offers real-time code completions.
• Documentation: Drafts technical documentation in Word.
• Data Analysis: Uses Excel + Python for forecasting and modeling.
• Meeting Summaries: Teams Copilot summarizes stand-ups and action items.

For Project Managers

• Task Tracking: Automates task updates and reminders.
• Presentation Creation: Builds stakeholder decks from project data.
• Email Drafting: Summarizes threads and drafts follow-ups.

For QA/Testers

• Bug Reporting: Drafts structured bug reports.
• Test Case Generation: Suggests test scenarios based on requirements

Industry Applications Uses

• Retail: Optimize shift scheduling and inventory
• Finance: Automate reporting and investment analysis
• Healthcare: Streamline clinical documentation
• Education: Personalize learning and automate grading
• Government: Draft public communications and manage budgets

Business Impact of Using Microsoft Copilot

1. Boosted Productivity

Automates repetitive tasks like drafting emails, generating summaries, or designing slides—freeing up more time for strategic work

2. Time Savings

• UK civil servants saved approximately 26 minutes daily (entry users averaged 37 minutes).
• TAL insurer saved up to 6 hours a week per employee.

3. Creativity & Quality Enhancements

Helps generate polished content, insightful visual designs, and improves presentation efficacy.

4. Seamless Integration

Works natively within existing Microsoft tools, reducing learning curves and ensuring smooth adoption.

5. Executive Workflow Optimization

Satya Nadella uses GPT-5–powered Copilot for prompts like meeting prep, project updates, time categorization, and decision analysis

Scaling Copilot—Building Intelligence for Your Organization

1. Copilot Connectors

Ingest data from ERP, CRM, and internal systems to enrich Copilot’s knowledge base, letting it reason over broader, organization-specific content, while respecting access controls.

2. Copilot Agents

Build or use prebuilt AI agents to automate workflows—e.g., employee onboarding, sales leads creation, IT requests—right from within Copilot.

Agents can:

• Take real-time actions (database updates, triggering flows)
• Tailor automation to your context

3. Copilot APIs

Use the Copilot Retrieval API to programmatically access enterprise data indexes and integrate AI capabilities into custom applications or workflows, with full compliance and governance.

How to Integrate Microsoft Copilot into Microsoft 365

Prerequisites

• Active Microsoft 365 subscription (E3/E5 or Business Standard/Premium).
• Microsoft Copilot license.
• Admin permissions for organizational deployment.

Integration Steps

1. Enable Copilot in Admin Center
   1. Go to Microsoft 365 Admin Center → Apps → Enable Copilot.
2. Assign Licenses
   1. Allocate Copilot licenses to users.
3. Configure Security & Compliance
   1. Ensure Conditional Access, MFA, and data governance policies are in place.
4. Deploy Across Apps
   1. Activate Copilot in Word, Excel, PowerPoint, Outlook, and Teams.

Where Copilot is Natively Embedded

• Word: Draft and edit documents.
• Excel: Analyze data and automate calculations.
• PowerPoint: Design presentations with AI.
• Outlook: Manage emails and calendars.
• Teams: Summarize meetings and manage tasks.
• OneNote: Organize notes and generate summaries.
• It also integrates with SharePoint, Planner, Project, and Power Platform for extended automation and data insights.

Why Microsoft Copilot is a Game-Changer

• By combining AI intelligence with enterprise data, Copilot transforms how businesses operate:
• Reduces manual effort.
• Improves decision-making.
• Enhances collaboration.
• Delivers measurable productivity gains across all departments.

Conclusion

In summary, Microsoft Copilot is not just a productivity tool—it’s a strategic AI partner that empowers professionals across industries. From automating mundane tasks to enhancing creativity and collaboration, Copilot is reshaping the future of work.

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

Empathy, Resilience, Innovation, and Speed: The Blueprint for Intelligent Healthcare Transformation

Forrester’s recent report, Becoming An Intelligent Healthcare Organization Is An Attainable Goal, Not A Lost Cause, confirms what healthcare executives already know: transformation is no longer optional.

Perficient is proud to be quoted in this research, which outlines a pragmatic framework for becoming an intelligent healthcare organization (IHO)—one that scales innovation, strengthens clinical and operational performance, and delivers measurable impact across the enterprise and the populations it serves.

Why Intelligent Healthcare Is No Longer Optional

Healthcare leaders are under pressure to deliver better outcomes, reduce costs, and modernize operations, all while navigating fragmented systems and siloed departments. The journey to transformation requires more than technology; it demands strategic clarity, operational alignment, and a commitment to continuous improvement.

Forrester reports, “Among business and technology professionals at large US healthcare firms, only 63% agree that their IT organization can readily reallocate people and technologies to serve the newest business priority; 65% say they have enterprise architecture that can quickly and efficiently support major changes in business strategy and execution.”

Despite widespread investment in digital tools, many healthcare organizations struggle to translate those investments into enterprise-wide impact. Misaligned priorities, inconsistent progress across departments, and legacy systems often create bottlenecks that stall innovation and dilute momentum.

Breaking Through Transformation Barriers

These challenges aren’t just technical or organizational. They’re strategic. Enterprise leaders can no longer sit on the sidelines and play the “wait and see” game. They must shift from reactive IT management to proactive digital orchestration, where technology, talent, and transformation are aligned to business outcomes.

Business transformation is not a fleeting trend. It’s an essential strategy for healthcare organizations that want to remain competitive as the marketplace evolves.

Forrester’s report identifies four hallmarks of intelligent healthcare organizations, emphasizing that transformation is not a destination but a continuous practice.

Four Hallmarks of An Intelligent Healthcare Organization (IHO)

To overcome transformation barriers, healthcare organizations must align consumer expectations, digital infrastructure, clinical workflows, and data governance with strategic business goals.

1. Empathy At Scale: Human-Centered, Trust-Enhancing Experiences

A defining trait of intelligent healthcare organizations is a commitment to human-centered experiences.

• Driven By: Continuous understanding of consumer needs
• Supported By: Strategic technology investments that enable timely, personalized interventions and touchpoints

As Forrester notes, “The most intelligent organizations excel at empathetic, swift, and resilient innovation to continuously deliver new value for customers and stay ahead of the competition.”

Empathy is a performance driver. Organizations that prioritize human-centered care see higher engagement, better adherence, and stronger loyalty.

Our experts help clients reimagine care journeys using journey sciences, predictive analytics, integrated CRM and CDP platforms, and cloud-native architectures that support scalable personalization. But personalization without protection is a risk. That’s why empathy must extend beyond experience design to include ethical, secure, and responsible AI adoption.

Healthcare organizations face unique constraints, including HIPAA, PHI, and PII regulations that limit the utility of plug-and-play AI solutions. To meet these challenges, we apply our PACE framework—Policies, Advocacy, Controls, and Enablement—to ensure AI is not only innovative but also rooted in trust.

• Policies establish clear boundaries for acceptable AI usage, tailored to healthcare’s regulatory landscape.
• Advocacy builds cross-functional understanding and adoption through education and collaboration.
• Controls implement oversight, auditing, and risk mitigation to protect patient data and ensure model integrity.
• Enablement equips teams with the tools and environments needed to innovate confidently and securely.

This approach ensures AI is deployed with purpose, aligned to business goals, and embedded with safeguards that protect consumers and care teams alike. It also supports the creation of reusable architectures that blend scalable services with real-time monitoring, which is critical for delivering fast, reliable, and compliant AI applications.

Responsible AI isn’t a checkbox. It’s a continuous practice. And in healthcare, it’s the difference between innovation that inspires trust and innovation that invites scrutiny.

2. Designing for Disruption: Resilience as a Competitive Advantage

Patient-led experiences must be grounded in a clear-eyed understanding that market disruption isn’t simply looming. It’s already here. To thrive, healthcare leaders must architect systems that flex under pressure and evolve with purpose. Resilience is more than operational; it’s also behavioral, cultural, and strategic.

Perficient’s Access to Care research reveals that friction in the care journey directly impacts health outcomes, loyalty, and revenue:

• More than 50% of consumers who experienced scheduling friction took their care elsewhere, resulting in lost revenue, trust, and care continuity
• 33% of respondents acted as caregivers, yet this persona is often overlooked in digital strategies
• Nearly 1 in 4 respondents who experienced difficulty scheduling an appointment stated that the friction led to delayed care, and they believed their health declined as a result
• More than 45% of consumers aged 18–64 have used digital-first care instead of their regular provider, and 92% of them believe the quality is equal or better

This sentiment should be a wakeup call for leaders. It clearly signals that consumers expect healthcare to meet both foundational needs (cost, access) and lifestyle standards (convenience, personalization, digital ease). When systems fail to deliver, patients disengage. And when caregivers—who often manage care for entire households—encounter barriers, the ripple effect is exponential.

To build resilience that drives retention and revenue, leaders must design systems that anticipate needs and remove barriers before they impact care. Resilient operations must therefore be designed to:

• Reduce friction across the care journey, especially in scheduling and follow-up
• Support caregivers with multi-profile tools, shared access, and streamlined coordination
• Enable digital-first engagement that mirrors the ease of consumer platforms like Amazon and Uber

Consumers are blending survival needs with lifestyle demands. Intelligent healthcare organizations address both simultaneously.

Resilience also means preparing for the unexpected. Whether it’s regulatory shifts, staffing shortages, or competitive disruption, IHOs must be able to pivot quickly. That requires leaders to reimagine patient (and member) access as a strategic lever and prioritize digital transformation that eases the path to care.

3. Unified Innovation: Aligning Strategy, Tech, and Teams

Innovation without enterprise alignment is just noise—activity without impact. When digital initiatives are disconnected from business strategy, consumer needs, or operational realities, they create confusion, dilute resources, and fail to deliver meaningful outcomes. Fragmented innovation may look impressive in isolation, but without coordination, it lacks the momentum to drive true transformation.

To deliver real results, healthcare leaders must connect strategy, execution, and change readiness. In Forrester’s report, a quote from an interview with Priyal Patel emphasizes the importance of a shared strategic vision:

“Today’s decisions should be guided by long-term thinking, envisioning your organization’s business needs five to 10 years into the future.” — Priyal Patel, Director, Perficient

Our approach begins with strategic clarity. Using our Envision Framework, we help healthcare organizations rapidly identify opportunities, define a consumer-centric vision, and develop a prioritized roadmap that aligns with business goals and stakeholder expectations. This framework blends real-world insights with pragmatic planning, ensuring that innovation is both visionary and executable.

We also recognize that transformation is not just technical—it’s human. Organizational change management (OCM) ensures that teams are ready, willing, and able to adopt new ways of working. Through structured engagement, training, and sustainment, we help clients navigate the behavioral shifts required to scale innovation across departments and disciplines.

This strategic rigor is especially critical in healthcare, where innovation must be resilient, compliant, and deeply empathetic. As highlighted in our 2025 Digital Healthcare Trends report, successful organizations are those that align innovation with measurable business outcomes, ethical AI adoption, and consumer trust.

Perficient’s strategy and transformation services connect vision to execution, ensuring that innovation is sustainable. We partner with healthcare leaders to identify friction points and quick wins, build a culture of continuous improvement, and empower change agents across the enterprise.

You May Enjoy: Driving Company Growth With a Product-Driven Mindset

4. Speed With Purpose and Strategic Precision

The ability to pivot, scale, and deliver quickly is becoming a defining trait of tomorrow’s healthcare leaders. The way forward requires a comprehensive digital strategy that builds the capabilities, agility, and alignment to stay ahead of evolving demands and deliver meaningful impact.

IHOs act quickly without sacrificing quality. But speed alone isn’t enough. Perficient’s strategic position emphasizes speed with purpose—where every acceleration is grounded in business value, ethical AI adoption, and measurable health outcomes.

Our experts help healthcare organizations move fast by:

• Designing for agility with composable architectures, MVP+ delivery models, and intelligent automation that compliantly accelerates time-to-value while maintaining clinical integrity
• Aligning digital transformation with value-based care goals, enabling organizations to shift from volume to value through strategic investments that integrate cloud platforms, interoperable systems, and advanced analytics
• Operationalizing AI responsibly to support equitable, secure, compliant, and scalable AI adoption

This approach supports the Quintuple Aim: better outcomes, lower costs, improved experiences, clinician well-being, and health equity. It also ensures that innovation is not just fast. It’s focused, ethical, and sustainable.

Speed with purpose means:

• Rapid prototyping that validates ideas before scaling
• Real-time data visibility to inform decisions and interventions
• Cross-functional collaboration that breaks down silos and accelerates execution
• Outcome-driven KPIs that measure impact, not just activity

Healthcare leaders don’t need more tools. They need a strategy that connects business imperatives, consumer demands, and an empowered workforce to drive transformation forward. Perficient equips organizations to move with confidence, clarity, and control.

Collaborating to Build Intelligent Healthcare Organizations

We believe our inclusion in Forrester’s report underscores our role as a trusted advisor in intelligent healthcare transformation. From insight to impact, our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of healthcare consumers, streamline operations, and improve the cost, quality, and equity of care.

We have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S., and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Our strategic partnerships with industry-leading technology innovators—including AWS, Microsoft, Salesforce, Adobe, and more—accelerate healthcare organizations’ ability to modernize infrastructure, integrate data, and deliver intelligent experiences. Together, we shatter boundaries so you have the AI-native solutions you need to boldly advance business.

Ready to advance your journey as an intelligent healthcare organization?

We’re here to help you move beyond disconnected systems and toward a unified, data-driven future—one that delivers better experiences for patients, caregivers, and communities. Let’s connect and explore how you can lead with empathy, intelligence, and impact.

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.

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}");
           }
        }
    }
}