GenAI & Agentic AI

AI & Automation Integration

Bring AI to the systems you already have — without rebuilding everything.

Discuss Your AI Project See Our Approach
Sound Familiar?

The Problem

AI is transforming every industry. But getting from hype to production is harder than the demos suggest.

All Hype, No Plan

AI is everywhere in the news, but your organization has no practical implementation strategy. Where do you even start? Which use cases deliver real value?

Manual Process Overload

Your team spends hundreds of hours per month on repetitive tasks that could be automated. Data entry, report generation, email processing — all done by hand.

Falling Behind Competitors

Your competitors are already using AI to move faster, serve customers better, and cut costs. Every month you wait is ground you'll have to make up later.

Stuck at Proof-of-Concept

You've tried AI projects before. They looked great in demos but never made it to production. The gap between a prototype and a reliable system is wider than expected.

Our Approach

How We Solve It

We bridge the gap between cutting-edge AI and real-world enterprise systems. No pie-in-the-sky demos — practical, production-ready AI integration that delivers measurable ROI.

LLM/GenAI Integration

Embedding large language models directly into your existing applications for content generation, summarization, classification, and intelligent search.

Agentic AI Systems

Building autonomous AI agents that handle multi-step tasks — from research and analysis to decision-making and execution — with human oversight where it matters.

Context Engineering

Designing optimal context architectures so AI models receive exactly the right information at the right time — maximizing accuracy while minimizing cost and latency.

RAG Implementations

Retrieval-Augmented Generation systems that ground AI responses in your company's actual data — documents, knowledge bases, databases — for accurate, trustworthy answers.

Workflow Automation

AI-powered automation of business processes — from intelligent document processing and email routing to automated reporting and data enrichment pipelines.

The Stack

Technologies We Work With

Modern AI tools and platforms, integrated into real-world systems.

GenAI Agentic AI Context Engineering LLMs Azure OpenAI RAG Python n8n LangChain
Pricing

Rates

Hourly rate

€100 / hour

Day rate

€800 / day

Prices are final — VAT is not applied (non-VAT payer). Project-based pricing available. First consultation is free.

Ready to Bring AI Into Your Business?

Stop watching from the sidelines. Let's identify the highest-value AI opportunities in your organization and turn them into production-ready solutions.

Get in Touch
FAQ

Frequently Asked Questions

Can you integrate AI into our existing .NET/Java application?
Yes, that's exactly where we specialize. We integrate AI capabilities directly into existing enterprise applications — whether they're built on .NET, Java, Python, or other stacks. You don't need to rebuild your system. We add AI as a new capability layer that works alongside your existing architecture, using APIs, microservices, or embedded SDK calls.
What's the difference between GenAI integration and building AI from scratch?
Building AI from scratch means training your own models — that requires massive datasets, specialized ML engineers, and significant compute budgets. GenAI integration leverages pre-trained foundation models (like GPT, Claude, or open-source LLMs) and adapts them to your specific use case through prompt engineering, context engineering, fine-tuning, and RAG. It's faster, cheaper, and delivers production-ready results in weeks instead of months.
What is context engineering?
Context engineering is the discipline of designing what information an AI model receives, in what format, and at what time. Think of it as architecting the "brain" around the AI — deciding which documents to retrieve, how to structure prompts, what conversation history to include, and how to manage token budgets. Good context engineering is often the difference between an AI that gives useless answers and one that feels like an expert. It's one of the most impactful and underrated aspects of AI integration.
How long does a typical AI integration project take?
A focused AI integration — like adding intelligent search or automated document processing — can be production-ready in 2-4 weeks. More complex projects involving agentic AI systems or multi-step workflow automation typically take 4-8 weeks. We always start with a discovery phase to identify the highest-ROI use case and deliver a working prototype quickly, then iterate based on real-world feedback.