AI for Legacy Applications
When AI must work inside systems that already matter
Your enterprise systems contain decades of business intelligence — encoded in databases, transaction logs, documents, and institutional knowledge. AI can unlock that intelligence. But only if it is integrated carefully into systems that cannot afford disruption.
Most AI implementations fail not because the models are wrong, but because they are built in isolation — disconnected from the production systems, data flows, and business processes they are supposed to improve.
Building a chatbot is straightforward. Connecting it to your ERP’s real-time inventory, your CRM’s customer history, and your compliance rules — while the system remains stable — is the hard part. That is where 19 years of enterprise system experience meets AI.
The gap between AI demo and AI in production
AI that works in a notebook does not automatically work inside an enterprise.
- Your team has experimented with ChatGPT or Azure OpenAI, but moving from demo to production — integrated with your actual data and workflows — has stalled.
- You have thousands of internal documents scattered across SharePoint, file servers, and databases. You want employees to query them conversationally, but no one has connected the pieces.
- You need a chatbot that accesses your product catalogue, order history, and pricing rules in real time — not one that gives generic answers.
- Your manufacturing operation needs quality inspection automation — computer vision on the production line, feeding results into your quality management system.
- You want to automate document processing, but the extraction needs to feed directly into your ERP with validated accuracy.
These are not AI problems. They are integration problems. And integration is what we have done for 19 years.
How we implemented AI into Enterprise Systems
Most AI failures are integration failures, not model failures. Our approach prioritises production safety over demo impressiveness.
Problem first, technology second
We start with the specific business problem and its measurable cost. If AI is not the right solution, we will say so. If a simpler approach works at lower cost, we will recommend that instead. Every implementation has a defined success metric before any code is written.
Proof of concept with real data
We build PoCs using your actual data and business scenarios — not generic demos with sample datasets. This validates feasibility and accuracy before committing to production integration.
Production integration without disruption
This is where our legacy system expertise matters most. We design integration architecture connecting AI to your existing .NET applications, databases, and workflows — with API layers, error handling, fallback strategies, and security controls that add capability without destabilising what already works.
Deployment with guardrails
Content filtering, output validation, audit logging, cost controls, and human-in-the-loop checkpoints for high-stakes decisions. AI is monitored for accuracy from day one — and improves through continuous feedback.
AI Technologies we implement
Generative AI
Azure OpenAI Service, OpenAI API, Semantic Kernel, LangChain
RAG & Search
Azure AI Search, vector databases, embeddings, hybrid search, citation tracking
Chatbots & Assistants
Azure Bot Service, Copilot Studio, Teams, WhatsApp
Computer Vision
Azure Custom Vision, Azure Computer Vision API, custom deep learning, OpenCV
NLP & Documents
Azure AI Language, Azure Document Intelligence, spaCy, Hugging Face
Our Capabilities
We implement AI as production-grade capabilities within existing enterprise applications — not as standalone experiments:
- Generative AI integration — automated report generation, AI-powered decision support, content creation. Integrated into .NET applications with guardrails: content filtering, prompt injection protection, output validation.
- Retrieval-Augmented Generation (RAG) — conversational access to internal documents, policies, and institutional knowledge. Custom embedding pipelines, hybrid search, source citation, access control. Connects to SharePoint, Azure Blob, file servers, databases.
- Intelligent chatbots — connected to production systems in real time: inventory, customer records, order status, pricing rules. Deployed in Teams, web, WhatsApp. Multi-turn conversations, human handoff, conversation analytics.
- Computer vision for manufacturing — quality inspection, defect detection, visual compliance checking. Handles variable lighting, high-throughput image streams, real-time pass/fail decisions feeding into quality management and ERP systems.
- Document intelligence — data extraction from invoices, purchase orders, compliance forms. Validated accuracy before entering downstream systems. Azure Document Intelligence, custom pipelines.
- NLP & text analytics — sentiment analysis, ticket classification, intelligent search, entity extraction from contracts and compliance documents.
Where We Apply AI
Manufacturing — computer vision quality inspection, predictive maintenance, production analytics, SAP data integration.
Enterprise operations — RAG-based knowledge systems for internal policies and SOPs, intelligent document processing, automated reporting.
Customer-facing applications — chatbots connected to live business data, conversational product search, AI-powered support routing.
Civic and government — AI-powered analysis of stakeholder inputs, automated report generation from co-creation processes.
The starting point is always the business problem and its cost — not the technology.
Engagement Models
AI Proof of Concept
4-8 week PoC with your real data. Validates feasibility before committing to production.
Production Integration
End-to-end: from PoC through production deployment with monitoring and guardrails.
AI Readiness Assessment
Evaluate your systems, data, and use cases. Deliver a prioritised roadmap with effort estimates.
Why choose us
Most AI companies build demos. We build integrations.
- We understand your legacy systems. Most AI companies are disconnected from enterprise reality. We have spent 19 years inside .NET production systems — we know how to add AI without breaking what already works.
- Our AI implementations connect directly to your .NET backend, SQL Server databases, Azure infrastructure, and business workflows. Not a separate tool. An enhancement to the systems your team already uses.
- Every implementation includes error handling, fallback strategies, security controls, cost governance, and monitoring. Production-grade from day one.
- We implement within the Microsoft ecosystem you are already invested in — Azure OpenAI, Azure AI Search, Azure Custom Vision, Azure Bot Service. No new vendor complexity.
The difference is not the AI. The difference is knowing where to put it.
Ready to add AI to systems your business already depends on?
Let us discuss how Generative AI, RAG, Computer Vision, or Intelligent Automation can solve a specific problem — integrated safely into your existing infrastructure.
