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AI Development &
LLM Integration

RAG systems, LLM integration, custom AI models, and industrial data analytics — from strategy to production-ready AI solutions. With experience from Apple, Siemens, and autonomous systems.

Our AI Services

From Generative AI to Industrial ML — we cover the full spectrum.

LLM Integration

Integrate GPT, Claude, Llama, and open-source models into your business processes. API integration, prompt engineering, and workflow automation.

RAG Systems

Retrieval-Augmented Generation for your enterprise data. Knowledge bases, document search, and context-aware AI responses.

Custom AI Models

Tailored models for your domain. Fine-tuning, transfer learning, and specialized training on your data.

Computer Vision

Image recognition, object detection, and visual inspection for industrial applications. PyTorch and state-of-the-art architectures.

Data Analytics & ML

Time series analysis, anomaly detection, and predictive maintenance. Classical machine learning through deep learning.

AI Strategy & Consulting

Use case identification, feasibility analysis, and roadmapping. We find the AI applications with the biggest impact.

Generative AI & LLM

Large Language Models are transforming how companies work with data and knowledge. We integrate LLMs seamlessly into your existing systems — from intelligent chatbots through document analysis to fully automated workflow control.

  • RAG pipelines for enterprise knowledge (documents, wikis, databases)
  • LLM API integration (OpenAI, Anthropic, open source)
  • AI agents & autonomous workflows
  • Prompt engineering & evaluation
  • Fine-tuning & domain-specific models

Custom AI & Machine Learning

Not every problem can be solved with an LLM. We develop custom models for your specific requirements — from predictive maintenance to visual quality inspection.

  • Time series analysis & predictive maintenance
  • Anomaly detection & pattern recognition
  • Computer vision & image recognition (PyTorch)
  • Log analysis & intelligent classification
  • From classical ML through deep learning

Data Pipeline & Infrastructure

Every AI is only as good as its data. We build the pipeline from source to model.

Data Collection

IoT sensors, embedded systems, CAN-Bus, MQTT, and REST. Capture data directly from machines and systems.

Data Processing

ETL pipelines, data enrichment, and feature engineering. PostgreSQL, TimescaleDB, Redis, and vector databases.

Visualization & KPIs

Dashboards, real-time monitoring, alerting, and historical analysis. Making data understandable for every audience.

AI Technology Stack

Proven frameworks and platforms for production-ready AI solutions.

LLM & GenAI

OpenAI / GPT Anthropic / Claude LangChain Llama / Open Source

ML & Deep Learning

PyTorch scikit-learn Hugging Face pandas / NumPy

Data & Infrastructure

PostgreSQL TimescaleDB Pinecone / pgvector AWS / Hetzner

From use case to production AI

1

Strategy

Identify use cases, assess feasibility, estimate ROI

2

Data

Connect data sources, build pipeline, ensure quality

3

Prototype

Develop PoC, train model, validate results

4

Production

Deploy model, build APIs, integrate into systems

5

Optimization

Monitoring, retraining, continuous improvement

Frequently Asked Questions

Do we need our own data for an AI project?

Not necessarily. For LLM integrations and RAG systems, existing documents and knowledge bases are often sufficient. For custom models, we need domain-specific data — but we also help build data collection infrastructure if it doesn't exist yet.

Cloud or on-premise for AI?

Both are possible. LLM APIs typically run in the cloud, but for sensitive data we also deploy open-source models on your own infrastructure. We find the right balance between performance, cost, and data privacy.

How long does a typical AI project take?

An LLM-based PoC can be ready in 2–4 weeks. Custom models typically need 2–3 months including data preparation and training. We always start with a quick prototype to validate the value early.

What does AI development cost?

That depends heavily on scope. An LLM integration can start with a manageable budget, while a complete ML system with data collection and model training requires a larger investment. We provide honest advice on which approach delivers the best ROI.

Still have questions? Get in touch

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