Microsoft AI Ecosystem

AI Development with
Microsoft Semantic Kernel

We architect and build production-grade AI solutions using Microsoft Semantic Kernel, Azure OpenAI, and the Microsoft Agent Framework — delivering real business automation, not just proof-of-concepts.

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Core AI Capabilities

Intelligent Solutions We Deliver

Built on the Microsoft AI stack — the same platform powering Microsoft 365 Copilot and enterprise AI globally.

Semantic Kernel Development

Custom plugins, planners, memory providers, and orchestration pipelines built with Microsoft Semantic Kernel SDK in C# .NET 8. Production-ready and enterprise-secure.

✓ Custom Kernel plugins & functions
✓ Stepwise & Function Call planners
✓ Semantic memory & vector search
✓ Azure AI Search integration

Agentic AI & Multi-Agent Systems

Autonomous AI agents that can reason, plan, and take actions across your business systems using the Microsoft Agent Framework (AutoGen / Magentic-One).

✓ Single & multi-agent orchestration
✓ Tool use & function calling
✓ Human-in-the-loop workflows
✓ Agent monitoring & guardrails

Azure OpenAI Integration

Secure, compliant integration of GPT-4o, GPT-4 Turbo, and embedding models via Azure OpenAI Service — with data residency in Canada Central or East US.

✓ Azure OpenAI Service setup
✓ Prompt engineering & testing
✓ Cost optimization & token management
✓ Canadian data residency options

RAG Pipeline Development

Retrieval-Augmented Generation systems that connect your enterprise knowledge base to LLMs — accurate, grounded answers from your own documents and data.

✓ Document ingestion & chunking
✓ Vector embeddings & search
✓ Azure AI Search / Qdrant
✓ Citation & grounding controls

AI-Powered SaaS Products

Build SaaS products with intelligent AI features baked in — smart document processing, AI assistants, automated workflows, and predictive analytics.

✓ In-app AI assistants & copilots
✓ Document classification & extraction
✓ Automated report generation
✓ Sentiment analysis & NLP

AI Security & Responsible AI

We implement responsible AI practices — content filtering, prompt injection protection, output validation, and compliance with Canadian AI governance guidelines.

✓ Azure Content Safety integration
✓ Prompt injection prevention
✓ AI audit trails & logging
✓ Secure data handling

Multi-Agent Workflow

// Multi-Agent Orchestration Example
var researchAgent = new ChatCompletionAgent {
  Name = "Researcher",
  Kernel = kernel,
  Instructions = "Research and summarize data..."
};

var writerAgent = new ChatCompletionAgent {
  Name = "Writer", Kernel = kernel,
  Instructions = "Draft a professional report..."
};

var chat = new AgentGroupChat(
  researchAgent, writerAgent) {
  ExecutionSettings = new() {
    TerminationStrategy = new
      ApprovalTerminationStrategy()
  }
};

await foreach (var response
  in chat.InvokeAsync()) {
  Console.WriteLine(response);
}
        

Microsoft Agent Framework

Multi-Agent AI Systems That Get Work Done

We build sophisticated multi-agent architectures where specialized AI agents collaborate autonomously — researching, writing, reviewing, and executing tasks across your business systems.

AutoGen / Magentic-OneMicrosoft's state-of-the-art multi-agent framework for complex, multi-step task completion.

Tool-Using AgentsAgents that call REST APIs, query databases, run code, and interact with your business applications.

Human-in-the-LoopConfigurable approval gates and escalation paths ensuring AI actions are auditable and controlled.

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Real-World Applications

AI Use Cases We've Delivered

📄

Legal Document Processing

AI agent that reads contracts, extracts key clauses, flags risks, and generates summaries — reducing lawyer review time by 70%.

🏥

Healthcare Intake Automation

RAG-powered assistant that answers patient queries from clinic knowledge base and routes complex cases to appropriate staff.

🏗️

Construction RFQ Automation

AI agent that reads project drawings, generates material take-offs, and auto-fills supplier RFQ forms — saving hours per project.

📊

Financial Report Generation

Multi-agent system that pulls data from accounting APIs, analyzes trends, and produces executive-ready financial narratives.

🛒

E-Commerce AI Assistant

Intelligent product recommendation engine and customer service agent integrated into existing e-commerce SaaS platforms.

🎓

eLearning Content Generation

AI pipeline that converts course material into structured lessons, quizzes, and adaptive learning pathways for LMS platforms.

FAQ

Common Questions About AI Development

Microsoft Semantic Kernel is an open-source SDK that lets developers integrate LLMs like GPT-4 into .NET, Python, and Java applications. We use it because it's production-tested, maintained by Microsoft, supports enterprise security requirements, and integrates seamlessly with Azure services — making it the ideal choice for Canadian enterprise clients.

A focused RAG pipeline or single AI agent can be production-ready in 4–8 weeks. More complex multi-agent systems or full AI-powered SaaS products typically take 3–6 months. We always start with a 2-week discovery and prototyping phase to validate the approach before full development.

Yes. We configure Azure OpenAI Service with the Canada Central region to ensure data residency compliance. We can also implement local models using Ollama or Azure AI Studio for air-gapped environments where data cannot leave your infrastructure.

Absolutely — this is one of our specialties. We have deep experience integrating Semantic Kernel into existing ASP.NET Core applications, connecting AI agents to SQL Server databases via Entity Framework, and building AI features that feel native to existing product experiences.

Azure OpenAI charges per token. For most business applications, monthly costs range from $50–$500 CAD depending on usage volume. We implement caching, token optimization, and smart prompt design to minimize costs. We provide a cost projection as part of every AI project proposal.

Free AI Strategy Session

Let's Design Your AI Solution

Book a free 60-minute session with our AI team. We'll review your use case, recommend the right architecture, and give you a realistic timeline and budget estimate.

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AI Development withMicrosoft Semantic Kernel