Microsoft's Build-your-own-copilot-Solution-Accelerator is a pragmatic blueprint for teams that want to turn the promise of generative AI into a working copilot grounded in their own data. Focused on a concrete scenario - preparing client advisors for better meetings - the repo shows how to combine Azure OpenAI, Azure AI Search, secure identities, and containerized services into a cohesive, repeatable solution. It is not a demo; it is a starting point you can deploy, adapt, and ship.
Key features & functionality
The repository distills a handful of features that translate directly to tangible gains in customer conversations and preparation time (see Key features).
- Data processing: Ingest and vectorize past conversations to build an AI-searchable memory for future queries and grounding.
- Semantic search: Azure AI Search powers RAG over structured and unstructured data, improving accuracy and traceability.
- Summarization: Azure OpenAI produces concise, actionable meeting summaries and next-step suggestions.
- Chat with data: A conversational interface, orchestrated with Semantic Kernel function calling, lets users ask natural questions and pull the right records at the right time.
Why this project matters
Client-facing work is awash in fragmented notes, emails, and call transcripts. Preparing for the next meeting often means sifting through unstructured data to reconstruct context, risks, and follow-ups. The accelerator's business scenario centers on a Woodgrove Bank client advisor who needs a single workspace that summarizes past conversations, surfaces portfolio details, and supports natural-language Q&A over client data.
By grounding generation in enterprise content and providing task-specific flows, the project targets real productivity for roles where conversations and context drive outcomes. See the Business Scenario and UI walkthrough in the README.
The solution in practice
The accelerator pairs Azure OpenAI for reasoning and summarization with Azure AI Search for retrieval-augmented generation (RAG). A data processing pipeline embeds prior conversation records, enabling semantic search and grounding. For live usage, the app stitches structured and unstructured sources together and uses Semantic Kernel to orchestrate function calls, so the assistant can fetch data or trigger actions when needed.
The result is a focused copilot for meeting prep: summaries, suggested topics, and a chat that can answer questions about a client's portfolio, tasks, and history, all backed by the organization's content. Explore the architecture in Solution overview and the deployment flow in docs/DeploymentGuide.md.
Under the hood
Languages and infrastructure choices reflect a pragmatic stack: TypeScript and Python for services and data handling; Bicep for IaC; and containerized components hosted on Azure Container Apps.
The repo's layout is familiar to modern cloud apps: browse src for application code, infra for Azure resources, tests/e2e-test for integration coverage, and docs for setup and operations. The root includes azure.yaml and app-azure.yaml to coordinate app services with Azure Developer CLI (azd). Identity and secrets are handled via Managed Identity and Key Vault.
# Minimal RAG-style query flow (illustrative)
query = "Next steps for Contoso meeting?"
docs = ai_search.search(query, top_k=5) # Azure AI Search
context = "\n\n".join(d.text for d in docs)
prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer:"
response = openai.chat(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}])
print(response.choices[0].message.content)
Azure resources are selected for elasticity and integration: Azure AI Services and Azure OpenAI for models, Azure AI Search for vector search, Azure SQL and Cosmos DB for structured and conversational data, Azure Container Registry and Container Apps for packaging and runtime, and Log Analytics for observability. The quota check and account setup guides streamline provisioning, while LocalSetupAndDeploy.md and TeamsAppDeployment.md cover development and distribution paths.
Where it fits: use cases
The Client Advisor scenario is finance-forward, but the architecture generalizes: sales reps prepping for customer calls, support leads summarizing ticket history before escalation, account managers planning renewals, or healthcare intake teams preparing case reviews.
Anywhere meeting outcomes improve with grounded summaries and fast answers, the pattern applies. Because the code and infra are modular, you can swap sample data for your own, evolve prompts, and add functions that hit internal systems. Cross-reference similar accelerators like Document knowledge mining and Conversation knowledge mining for adjacent patterns (Microsoft, 2025).
Community & contribution
The project is active and versioned, with an update on 2025-04-24 that moved the Research Assistant scenario to a separate branch and focused the main branch on Client Advisor. Contributions follow Microsoft's standard flow: review CONTRIBUTING.md for the CLA process and etiquette, and see the Issues and Pull requests tabs to join discussions. The repository adopts the Microsoft Open Source Code of Conduct (Microsoft, 2025).
Usage & license terms
Code is released under the MIT License, which allows broad reuse, modification, and distribution with attribution. The README includes explicit disclaimers: the solution is a proof of concept, ships without warranty, uses synthetic sample data, and is not intended for high-risk uses. Use of any Microsoft cloud services is governed by their respective Product Terms, and you must comply with export laws. For responsible AI commitments and transparency notes, see TRANSPARENCY_FAQ.md (Microsoft, 2025).
About Microsoft's open source program
The accelerator comes from a company that is deeply invested in open source tooling and practices. Microsoft maintains thousands of public repos across languages and domains, from VS Code to Semantic Kernel and GraphRAG. The organization's profile and site outline governance and community norms, including a Code of Conduct and open source program resources (Microsoft, 2025).
Impact and what comes next
Solution accelerators compress the distance between idea and implementation. By encoding best practices for RAG, function calling, and secure cloud deployment, this repo reduces risk and time-to-value for AI copilots.
Expect deeper integration patterns to emerge: topic-graph indexing with GraphRAG for complex knowledge navigation, richer agent capabilities via Semantic Kernel planners, and first-class enterprise deployment guides for Teams or line-of-business portals. The docs already cover quota checks, manual app registration, and reusing observability; the groundwork for scale and compliance is here.
Conclusion
If your organization wants a working starting point for a meeting-prep copilot - built on Azure, grounded in your data, and secured with enterprise patterns - this accelerator is a strong foundation. Read the README, scan the docs, and try the Quick deploy path. Then make it yours.
Explore the repository and join the community:
https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator
Build Your Own Copilot, From Template to Production