What we do
At AI Research & Technology Lab GmbH, we enable pioneering research projects through advanced technology development. We work with research institutes and innovative companies to build AI systems that turn data and ideas into decisions and experiments – not just demos.
Our work ranges from AI decision systems that sit on top of existing dashboards and data sources, to AI-powered experiment and lab platforms that make complex studies actually runnable in practice.
You already have dashboards, metrics, and reports; we build the AI systems that can use them to run structured what-if scenarios and support real decisions.
AI decision systems for real-world decisions
Many organisations already track everything: KPIs, funnels, survey results, financial data. What’s missing is a decision layer: a system that can query this data on demand, run structured “what-if” scenarios, and surface recommendations a human can actually act on.
We design and build AI decision systems that:
- connect to your existing tools and data sources via well-defined APIs,
- run controlled tool-using agents that stay within guardrails,
- produce traceable briefings instead of opaque answers, and
- keep a decision and reasoning log so experiments and decisions can be audited or revisited later.
This makes it possible to move from “we have dashboards” to “we have AI that helps us decide what to do next.”
Experiment & lab platforms
A core focus of the lab is building AI-powered experimental systems for social science, human-robot, and human-AI interaction research.
Examples include:
- Social robot interviewers – the complete system behind Willi, a social robot that interviews visitors at an innovation lab: on-robot AI skill, backend, tablet app, and management tools.
- Multi-module online experiments – where one system coordinates shopping assistants, chat-based interviewers, and survey components into a single study flow.
- Multi-LLM pipelines for automated classification of text survey responses – with human reconciliation on disagreement and continuous learning from that feedback.
- Custom lab tooling – including precise sensor synchronization, logging infrastructure, and control software that ties together robots, screens, sensors, and backends.
Behind each visible system is a set of enabling technologies: domain models, tool APIs, simulators, and experiment logic that make studies repeatable, adaptable, and analysable instead of one-off prototypes.
Expert engineering as a foundation
Under everything we build is a commitment to expert engineering:
- Correctness and reliability – tested components, careful treatment of edge cases, and clear contracts between systems.
- Performance where it matters – from low-level Zig for performance-critical components to efficient Python backends and streaming interfaces.
- Reproducibility – versioned infrastructure, experiment configurations, and data pipelines so results can be revisited and extended.
- Long-term maintainability – code and systems that can be handed over to in-house teams, no black boxes.
This combination of research awareness and engineering discipline allows the lab to take on projects that sit between a research prototype and a production system – exactly where many ambitious AI and HRI experiments live.
Working with the lab
We typically collaborate with:
- research groups running complex online or lab experiments,
- innovation labs exploring new forms of human-AI interaction, and
- organisations that already have serious data and dashboards, but need an AI layer that helps them decide and experiment, not just chat.
If you’re planning a project that needs AI decision systems, experiment platforms, or custom lab infrastructure, we’re happy to explore whether it’s a good fit for the lab.