Abp Framework Development Team for a medical technology company in Taiwan
How Facile Technolab helped Medical Device CTO Reduced Diagnostic Errors by 25% with Custom SaaS on Blazor & Azure.
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You have a running ASP.NET Core application. You want to add an AI feature - a document chatbot, a smart search, an automated workflow, or a customer-facing assistant. You don't want to rebuild the application. You want AI added to what you already have, working reliably in production, within a realistic timeline and budget. That is what we do. Facile Technolab adds AI capabilities to existing ASP.NET Core applications using Microsoft Agent Framework 1.0 (the production successor to Semantic Kernel), Azure OpenAI Service, and Azure AI Search - integrated into your existing codebase, your existing authentication, your existing data.
Replace manual, repetitive multi-step processes with AI agents that can reason, call your existing APIs, make decisions, and complete tasks - invoice processing, data extraction, lead qualification, support ticket triage. Built on Microsoft Agent Framework 1.0 and deployable to Azure Container Apps.
Upload your PDFs, manuals, contracts, or knowledge base. Users ask questions in natural language and get accurate answers drawn from your actual documents - not hallucinated responses. Built using RAG (Retrieval-Augmented Generation) with Azure AI Search and Azure OpenAI.
A chatbot that knows your business - connected to your database, your CRM, your product catalog, or your internal documentation. Responds accurately, escalates when needed, and stays within the boundaries you define. Deployed within your existing ASP.NET Core application.
Replace keyword search with semantic understanding — users find what they mean, not just what they typed. Relevant for product catalogs, support knowledge bases, internal document libraries, and job boards. Integrates with your existing ASP.NET Core search infrastructure.
The most important thing to understand about adding AI to an existing ASP.NET Core application is that it does not require a rewrite. AI features integrate at the application layer — they call your existing APIs, read from your existing database, and authenticate through your existing identity system.
A typical engagement starts with a 2-week discovery: understanding what your application currently does, where AI would deliver measurable value, and what the integration architecture looks like. We assess your existing codebase before quoting — scope surprises in AI integration projects almost always come from assumptions made without reading the code.
| Agent framework: | Microsoft Agent Framework 1.0 (Microsoft.Agents.AI) |
|---|---|
| AI abstraction: | Microsoft.Extensions.AI |
| Models: | Azure OpenAI (GPT-4o, GPT-5.5), Azure AI Foundry |
| Vector store: | Azure AI Search (hybrid vector + keyword) |
| Backend: | ASP.NET Core, Minimal APIs, Azure FunctionsI |
| Auth: | Azure Managed Identity, existing app auth |
| Observability: | OpenTelemetry, Azure Application Insights |
| Hosting: | Azure App Service, Azure Container Apps |
| Compliance: | HIPAA-eligible (Azure OpenAI BAA available) |
We do not sell a single AI product. Instead, we design and develop AI capabilities that plug into your custom Microsoft applications and workflows. These are some of the ways we use AI to help operations-heavy teams work smarter.
Define where AI can add measurable value in your .NET and Azure systems and create a practical roadmap.
Forecast demand, risk, or behavior using models deployed on Azure and integrated into your dashboards.
Add AI assistants to your portals or products to answer questions, collect data, and route requests.
Personalize offers and content inside your applications based on behavior and segment data.
Use image and document understanding for scenarios like quality checks, ID verification, and form capture.
Clean and structure your data so it is ready for AI, analytics, and reporting.
Build and train custom models for your specific classification, prediction, or scoring problems.
Connect Azure AI and custom models to your .NET APIs, jobs, and UI components.
Automate repetitive steps in your processes by combining business rules and AI.
We don't propose a new platform or a rebuild. Every AI engagement starts with reading your existing ASP.NET Core application - understanding how it's structured, where the data lives, and how authentication works - before we write a line of AI code. The AI features we build use your existing Entity Framework models, your existing SQL Server or Azure SQL database, and your existing identity system. There is nothing to replace.
We used ChatGPT API to automate complex event planning workflows in a marketplace platform, reducing manual administrative overhead by 40%. We integrated TensorFlow-based image recognition into a fitness application for a US-based client. We built a medical imaging SaaS on Blazor and Azure for a Taiwan medical device company, reducing diagnostic errors by 25%. These are not AI experiments - they are production systems used by real businesses every day.
We work with Microsoft Agent Framework 1.0 (the production successor to Semantic Kernel, GA April 2026), Azure OpenAI Service (GPT-4o, GPT-5.5), Microsoft.Extensions.AI, and Azure AI Search. We do not use frameworks that Microsoft has deprecated or tools that require your application to leave the Azure security boundary.
Azure OpenAI Service is available under Microsoft's Business Associate Agreement - making AI features in ASP.NET Core healthcare applications HIPAA-eligible. We have shipped AI-enabled applications in healthcare contexts and can design your AI integration to meet your compliance requirements from the architecture phase, not as an afterthought.
The AI feature that delivers the most value depends on what your ASP.NET Core application already does and who uses it. Here is how AI integration typically looks in the industries we know best.
Existing ASP.NET Core patient portals and clinical tools gain AI through: document extraction from clinical notes and referral letters, triage support that classifies incoming requests before human review, and predictive analytics that surface patients requiring follow-up. All within Azure's HIPAA-eligible infrastructure.
Existing .NET financial platforms gain AI through: risk scoring models embedded into underwriting workflows, document Q&A on policy documents and contracts, anomaly detection in transaction streams, and intelligent customer support that handles common queries before escalation.
Existing ASP.NET Core operations systems gain AI through: predictive maintenance models that surface equipment anomalies before failure, natural language search across technical documentation and manuals, and automated quality report generation from structured sensor data.
Existing SaaS platforms built on ASP.NET Core gain AI through: semantic search replacing keyword search across user content, AI assistants embedded in the product UI, automated onboarding workflows triggered by user behavior, and AI-generated summaries of user activity for reporting dashboards.
Not every AI use case requires custom development. Here is an honest guide to when this service is the right fit — and when it is not.
You need custom AI integration when:
You may not need custom integration when:
If you are unsure which category your use case falls into, that is exactly what our discovery conversation is designed to determine — before either of us commits to anything.
We know that AI projects can feel ambiguous before they start. Here is exactly what the early stages of an engagement look like.
We talk through your existing ASP.NET Core application — what it does, what data it holds, where the manual processes are that AI might replace. No technical deep-dive required from your side at this stage. The goal is understanding your situation well enough to assess whether and how AI adds value.
We review your codebase (under NDA) to understand the existing architecture — authentication, data access patterns, API structure. We identify the cleanest integration points for the AI feature you need and flag any dependencies that would affect the approach.
You receive a specific proposal: what we will build, how it will integrate with your existing application, what the timeline is, and what it costs. Fixed-price for scoped engagements, monthly retainer for ongoing AI feature development.
Development runs in two-week sprints with a working demo at the end of each sprint. You see the AI feature working against your actual data before it reaches production.
How Facile Technolab helped Medical Device CTO Reduced Diagnostic Errors by 25% with Custom SaaS on Blazor & Azure.
Read Full Case Study
How Facile Technolab built a digital legal search SaaS platform for an Australian startup - ASP.NET Core, Azure, and full-stack delivery from MVP to launch.
Read Full Case Study
How Facile Technolab built a cruise management SaaS for a Croatian travel client connecting ship owners, agents, and passengers on a single platform.
Read Full Case StudyAccelerate your business growth with our client-centric software development services.
Our enterprise application development services focus on building scalable, reliable systems that evolve with organizational growth and operational demands.
Our cloud development services support cloud-native application development as well as modernization of existing systems for cloud platforms.
Our SaaS development services support the creation of scalable, multi-tenant platforms that evolve with user demand and business growth.
Our MVP development services enable teams to test assumptions, gather early user feedback, and iterate faster without overinvesting in full-scale product builds.
Our legacy software modernization services support enterprises in transforming outdated systems into scalable, maintainable, and future-ready applications.
Our team delivers end-to-end product development services that support new product launches, product reengineering, and modernization of existing software platforms.
For a focused feature - a document Q&A chatbot or a semantic search replacement - typically 4–8 weeks from discovery to production. Workflow automation agents with complex multi-step logic take 8–16 weeks depending on the number of API integrations involved. We provide a scoped timeline after the discovery phase, not before - AI integration timelines depend heavily on your existing codebase's structure and your API availability.
No. AI integration in ASP.NET Core works by adding new services to your existing dependency injection container and calling them from your existing controllers or endpoints. Your database, your authentication, your deployment pipeline, and your existing functionality all remain unchanged. The AI feature is additive, not a replacement.
Yes, specifically Azure OpenAI as distinct from OpenAI's direct API. Azure OpenAI is available under Microsoft's Business Associate Agreement (BAA), making it HIPAA-eligible for healthcare use cases. Your data stays within Azure's security boundary - it is not used for model training, and you retain full data residency control through Azure region selection. We have shipped AI-enabled applications in healthcare contexts using this configuration, including a medical imaging platform for a Taiwan-based client.
Microsoft Agent Framework 1.0 (released April 2026) is Microsoft's production-ready SDK for building AI agents in .NET — the successor to Semantic Kernel and AutoGen, which it replaces with a unified, long-term-supported API. If you have read about Semantic Kernel, Agent Framework is where Microsoft now directs all new .NET AI development. We use it because it integrates natively with ASP.NET Core's dependency injection, has first-class Azure OpenAI support, ships with built-in OpenTelemetry observability, and carries Microsoft's long-term support commitment — the things that matter for production systems we hand over to clients.
A scoped, focused AI feature (document Q&A or semantic search) typically runs $15,000–$40,000 for design, integration, and production deployment. Workflow automation agents with multiple integrations typically run $35,000–$80,000 depending on complexity. Ongoing Azure OpenAI costs are usage-based — a typical SMB document Q&A system with moderate usage runs $100–$500/month in Azure OpenAI API costs. We provide fixed-price estimates after discovery for scoped projects and monthly retainer models for ongoing AI feature development.
If your organization already uses .NET and Azure and you are exploring AI, we can help you move from ideas to production features. Whether you are in Manufacturing, Healthcare, Financial Services & Insurance, or Event Management, we will design and build AI capabilities that fit your existing systems and deliver measurable outcomes.
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