With .NET, automate and accomplish complex tasks using AI — with the tools, platforms, and services your team already knows. From chat apps and vector search to MCP servers and intelligent agents.
Chat Apps
LLM-powered conversations
Vector Search
Semantic & RAG retrieval
AI Agents
MCP & function calling
Generative AI
Text, code & image generation
Millions of developers use .NET to create applications that run on the web, on mobile and desktop devices, or in the cloud. By integrating AI into .NET, you get the best of both worlds.
A unified story for building web UIs, APIs, and full applications — from a single ecosystem.
Supported on Windows, macOS, and Linux. Build once and run your AI apps everywhere.
Is open-source and community-focused with a vast ecosystem of libraries and tooling.
Runs on the most popular web servers and cloud platforms including Azure, AWS, and GCP.
Visual Studio and VS Code provide powerful tooling to edit, debug, test, and deploy AI apps.
Microsoft.Extensions.AI provides unified abstractions to swap AI providers with minimal code changes.
.NET libraries support a wide range of AI service providers via Microsoft.Extensions.AI, enabling you to build with the platform that best fits your needs.
Direct integration with OpenAI's models including GPT-4o, GPT-4, and DALL-E for generation and vision tasks.
Enterprise-grade OpenAI models hosted on Azure with enhanced security, compliance, and private networking.
Microsoft's managed platform for building and deploying AI agents at scale with governance and monitoring.
Access to a wide range of models available through GitHub's AI model marketplace for development and testing.
Run open-source models locally — Llama, Mistral, Phi-3 and more — for privacy-first and offline scenarios.
Google's multimodal AI models supporting text, images, audio, and code in a single unified API.
AWS's fully managed service for accessing foundation models from Amazon, Anthropic, Meta, and more.
Any AI provider usable with Microsoft.Extensions.AI is also compatible with .NET Agent Framework.
The opportunities with AI are near endless. Here are solutions we build using AI in .NET applications.
Create virtual agents or chatbots to talk with your data and generate content and images. Build LLM-powered chat apps backed by your business data using RAG.
Discuss with usIdentify objects, classify content, and extract meaning from images and video using Azure AI Vision and multimodal LLM models.
Discuss with usUse synthesized voices to interact with customers. Build voice-enabled .NET applications with Azure AI Speech — text to speech and speech to text.
Discuss with usLabel the severity of customer-reported issues, categorise support tickets, detect sentiment, and classify documents automatically using ML.NET or Azure AI Language.
Discuss with usAutomatically perform the next step in a workflow as tasks are completed. Use AI function calling and MCP servers to orchestrate multi-step business processes.
Discuss with usEnd-to-end AI development services built on .NET — from proof of concept to production.
We build conversational .NET applications powered by Azure OpenAI and GPT models. Using retrieval-augmented generation (RAG) with your business data, we create chat experiences that are accurate, contextual, and safe.
We implement vector stores using Azure AI Search, Qdrant, and Milvus to enable semantic search across your documents and data. Combined with embeddings and RAG, your applications surface the right information every time.
We build .NET AI agents with Semantic Kernel and the Model Context Protocol (MCP) C# SDK. Our MCP servers expose your business tools and data to AI clients, enabling intelligent multi-step automation backed by your own logic.
We integrate generative AI into your existing .NET applications — content generation, code assistance, document summarisation, and image generation using OpenAI DALL-E and Azure AI. Rapid integration with Microsoft.Extensions.AI abstractions.
We automate complex business workflows by connecting .NET applications to AI models. From classifying support tickets and routing workflows to generating reports and extracting data from documents using Azure Document Intelligence.
We train and deploy custom machine learning models using ML.NET — classification, regression, anomaly detection, and recommendation systems — embedded directly into your .NET applications without external dependencies.
The Model Context Protocol (MCP) is an open standard that enables AI models to securely connect to tools, data, and services. We build MCP servers using the official MCP C# SDK (Microsoft and Anthropic) to expose your business logic to AI agents.
Our MCP server implementations connect to Azure Container Apps, Azure OpenAI, and Visual Studio — making your .NET tools first-class citizens in any AI workflow.
MCP C# SDK
Microsoft and Anthropic's official C# SDK for building MCP servers and clients in .NET.
Azure Container Apps
Deploy MCP servers to Azure Container Apps for scalable, production-grade AI tool hosting.
ASP.NET Core MCP
Build MCP servers with ASP.NET Core and expose HTTP-based endpoints for AI agent consumption.
VS Code MCP debugging
Register, debug, and test MCP servers directly in VS Code with the official MCP developer guide.
Utilise the growing ecosystem of .NET AI libraries — from unified abstractions to specialised SDKs.
Unified AI abstraction layer for .NET — swap providers without changing your app code.
Microsoft's open-source SDK for orchestrating AI agents, plugins, and memory in .NET.
Official OpenAI .NET library connecting to OpenAI or Azure OpenAI REST APIs from any .NET app.
Access Azure AI Foundry models including open-source LLMs and multimodal models via a unified SDK.
Train and deploy custom machine learning models natively in .NET without external ML frameworks.
Abstraction layer for CRUD and vector search operations across vector store implementations.
Official C# SDK for Model Context Protocol — build MCP servers and clients in .NET.
Build and deploy AI agents at scale on Azure with governance, monitoring, and enterprise controls.
The recommended sequence of quickstarts for building production AI apps with .NET:
Tokenization is the foundation of every production LLM application. The Microsoft.ML.Tokenizers library gives .NET developers a comprehensive, high-performance toolkit to count, split, encode, and decode text — exactly as AI models interpret it.
Assemblysoft integrates tokenization into every AI application we build — enabling precise cost estimation, intelligent context window management, and text preprocessing pipelines that keep your applications fast, accurate, and within budget.
Token counting & cost estimation
Count tokens before sending to an AI model — enabling accurate cost forecasting and API budget control across GPT-4o, Llama, BPE, and more.
Intelligent text trimming
Trim prompts or documents to fit context windows precisely from either end — avoiding truncation errors and maximising model performance.
Encode & decode pipelines
Bidirectional encoding: convert text to token IDs for AI consumption and decode IDs back to human-readable text for inspection and audit.
Multi-algorithm support
Tiktoken, BPE, Llama, and CodeGen tokenizers — supporting every major LLM family from a single, unified .NET API.
We work with all major tokenizer models used by today's leading LLM providers:
Used by GPT-4o, GPT-4, and GPT-3.5 models. The gold standard for OpenAI and Azure OpenAI token counting.
Byte-pair encoding — the foundational algorithm behind GPT-2 and many LLMs. Compresses repeated sequences into vocabulary tokens.
Powers Meta's Llama family. Essential when running Llama, Mistral, or Phi-3 locally via Ollama or Azure AI Foundry.
Optimised for code generation workloads — compatible with programming-focused models requiring code-aware tokenization.
Key tokenizer operations available in .NET
We are a UK-based Microsoft Partner with certified .NET and Azure developers. Tell us your AI challenge and we'll build something remarkable together.
