AI Integration and Consulting
We don’t build AI demos. We integrate AI into real systems — your products, your processes, your infrastructure. LLMs, custom models, predictive optimization — serverless in the cloud, on-premises, or right at the edge. And if a challenge is better solved without AI, we’ll tell you that too.
When we’re the right fit
You want AI in your existing systems
Not as an isolated prototype, but as a core part of your software. We bring AI capabilities into your existing architecture — with clean APIs, proper error handling, and real monitoring.
You need your own model — not just an API
Anyone can wrap ChatGPT. When your domain requires specialized knowledge or your data demands specific models, we build custom models tailored exactly to your use case.
You're not sure where AI actually makes sense
Not every task needs AI. We evaluate your use cases honestly — technically and economically. What's worth it? What's hype? Where's the biggest ROI?
Your model runs in a notebook — but not in production
The path from Jupyter notebook to production-ready service is longer than you'd think. We get your models into scalable, observable deployments — with CI/CD, monitoring, and automated retraining.
You need AI at the edge or on-premises
Not all AI belongs in the cloud. Latency, data privacy, or lack of connectivity — we deploy models on edge devices and local infrastructure where it's needed.
You want to build AI expertise in your own team
We make ourselves redundant — on purpose. Through pair programming, code reviews, and hands-on workshops, we enable your team to evolve AI solutions independently.
What we do
AI Strategy & Use Case Assessment
Before we write code, we figure out if and where AI makes sense for you. No 80-page strategy paper — just an honest assessment with clear recommendations.
- Use case analysis: Which of your processes actually benefit from AI? We prioritize by feasibility, effort, and business impact
- Data assessment: Do you have the data you need? Quality, volume, accessibility — we find out where you stand
- Build vs. buy: Custom model, fine-tuning, or API integration? We recommend the approach that makes economic sense
LLM and SLM Integration
Large and Small Language Models are changing how software interacts with users and data. We integrate them where they create real value.
- RAG architectures: Retrieval-Augmented Generation for systems that work on your data — not the internet
- Fine-tuning: Adapting foundation models to your domain and terminology
- Agentic workflows: Autonomous AI agents that use MCP (Model Context Protocol) and tool integration to handle multi-step tasks across your system landscape
- API management for AI services: As an official Gravitee partner, we use Gravitee as our API gateway — for secure routing, rate limiting, and access control of your AI endpoints
Model Adaptation & Specialization
We don’t build foundation models — but we make them work for your use case. Through fine-tuning, prompt engineering, and targeted retraining, we adapt existing models to your domain.
What that looks like in practice:
- Fine-tuning & retraining of foundation models on your data, your terminology, and your quality requirements
- Predictive optimization based on weather, consumption, and sensor data for a smart energy platform
- Models for constrained hardware — optimized for edge devices with limited memory and compute
Edge & On-Premises AI
Not all AI can or should run in the cloud. We bring models where they’re needed.
- Edge deployment: Optimized models on embedded hardware — performant even with limited resources
- On-premises operation: AI systems on your own infrastructure when data privacy or regulation requires it
- Hybrid architectures: Training in the cloud, inference at the edge — we find the right split
MLOps & Production Operations
A model is only as good as its operations. We make sure your AI systems run reliably — not just on launch day.
- CI/CD for models: Automated training, testing, and deployment — reproducible and versioned
- Monitoring & drift detection: Catching model quality degradation before your users notice
- Scaling: From proof-of-concept to production load — on AWS, Azure, or your own infrastructure
Where we’ve proven this
Smart Energy Platform
Intelligent self-consumption optimization based on weather data and consumption forecasts. Predictive models on edge hardware (ARM9, 128 MB RAM) for real-time decisions without cloud dependency. 9 OEM partners, 3 countries.
Edge AI · Predictive Optimization · Java · Embedded Linux
Our AI Stack
Pragmatic over trendy. We use tools we’ve proven in production.
Java / Kotlin
Python
LangChain4J
LLMs / SLMs
AWS Bedrock
Azure AI
Hugging Face
Ollama / vLLM
MCP
Gravitee
Edge / Embedded
AI project in mind?
Whether it’s a use case assessment, LLM integration, or custom model — we’ll listen and tell you honestly what makes sense.