Applied AI Research
Research
Autonomous experimentation, perceptual AI, and AI-native methodology. Research that ships, not research that publishes.
Most research stays in papers. Interlusion's research reaches production. Every investigation starts with a concrete engineering question and ends with working systems, validated methods, or tools that feed directly into client work and internal products.
The approach is grounded in the German engineering tradition and a Master's in Intelligent Systems. Rigorous methodology, reproducible results, practical outcomes. No hand-waving, no perpetual prototypes.
AI research moves fast enough that waiting for the literature means falling behind. Interlusion runs its own experimental programmes — autonomous agents designing and executing experiments, visual systems exploring perceptual primitives, new interaction patterns tested in real products.
The result is a research function that compounds: every finding improves the tools, the tools improve the next investigation, and clients benefit from methods nobody else has deployed yet.
Capabilities
Research that reaches production
Four research domains, each grounded in engineering practice. From autonomous experimentation to perceptual AI.
- Autonomous Experimentation
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AI agents that design experiments, execute them, analyse results, and iterate — under human direction. Hypothesis generation, parameter sweeps, and evaluation pipelines that run continuously. Research velocity that a traditional lab cannot match.
- Visual Primitives & Perception
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Investigating the fundamental building blocks of visual perception — how machines and humans parse visual information differently. Spatial hierarchies, semantic compression, and the structure of seeing. Early-stage research with a solid theoretical foundation.
- AI-Native Research Methodology
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Traditional scientific method adapted for AI-native workflows. Structured context architectures for research projects, agent-assisted literature review, automated experiment tracking, and reproducibility infrastructure. The method itself is the innovation.
- Research-to-Product Pipeline
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The gap between research finding and production feature is where most value dies. Every research output has a defined path to integration — into internal tools, client products, or published methodology. No shelf-ware.
Process
How a research engagement works
Applied research requires structure. Four phases, each with concrete deliverables.
01
Problem Framing
Define the research question in engineering terms. What needs to be true for this to create value? Literature review, prior art analysis, and feasibility assessment. The sharpest gains come from asking the right question.
02
Experiment Design
Design the experimental framework: variables, controls, evaluation metrics, and success criteria. Build the infrastructure — data pipelines, measurement tools, automated execution environments.
03
Autonomous Execution
Agent-driven experimentation cycles. AI systems execute experiment batches, collect results, and surface patterns. Human researchers review findings, adjust parameters, and direct the next round.
04
Synthesis & Transfer
Consolidate findings into actionable outputs: validated methods, working prototypes, or integration blueprints. Every research engagement produces something that ships.
Engagement Models
Structured research, flexible scope
Research engagements range from focused investigations to long-term programmes. The structure adapts to the question.
4–8 weeks
Focused Investigation
A single research question, pursued to a clear answer. Includes problem framing, experiment design, execution, and a written report with actionable recommendations. Ideal when the question is well-defined and the organisation needs evidence before committing.
Best for: CTOs evaluating new approaches, product teams with specific unknowns
3–12 months
Applied Research Partnership
Ongoing research programme aligned with product or business goals. Multiple investigation threads, regular reporting, and direct integration of findings into engineering workflows. Interlusion functions as an embedded research arm — without the overhead of building one internally.
Best for: Companies without dedicated R&D, innovation teams, technical founders
Monthly
Research Retainer
Continuous access to research capability. Emerging questions get investigated as they arise. Trend monitoring, competitive technical analysis, and proactive recommendations. For organisations that need to stay at the frontier without dedicating headcount.
Best for: AI-forward companies, scaling engineering teams
The Difference
Lab discipline, startup pace
Interlusion combines the methodological rigour of institutional research with the speed and pragmatism of a product company. Every investigation is structured, reproducible, and aimed at production impact. This is not a university lab chasing citations — it is an engineering research function that measures success by what ships.
- Intelligent Systems specialisation
- M.Sc.
- Years engineering experience
- 25+
- Autonomous experimentation methodology
- Auto
- Every finding has a path to production
- Ship
"Research without deployment is just expensive curiosity. Every investigation we run has a shipping address."
Applied research, grounded in engineering. Led by Prof. Dr. Gutensieben, Head of Research.
Common questions
Every research programme starts with an engineering question and ends with something that can be used — a validated method, a working prototype, or a tool that integrates into production systems. Interlusion does not pursue research for its own sake. The measure of success is deployment, not publication.
Autonomous experimentation driven by AI agents. Agents generate hypotheses, design experiments, execute them, and analyse results under human direction. The researcher sets the direction and evaluates findings. The agents handle the volume. This compresses traditional research timelines by an order of magnitude.
Interlusion's Head of Research — an AI personality that leads research initiatives, designs experiments, and synthesises findings. Gutensieben operates under human supervision and embodies the company's research approach: rigorous methodology, practical focus, and a healthy scepticism of hype. Think of it as a principal investigator that never sleeps.
Selectively. When findings benefit the broader community, Interlusion publishes methodology and results. But publication is a side effect, not the goal. Priority goes to findings that create competitive advantage for clients or improve internal tools. Open methodology, proprietary application.
Current focus areas: autonomous experimentation methodology, visual primitives and perceptual AI, AI-native research workflows, and context architecture for agent systems. The domains evolve based on what creates the most engineering leverage.
That is the explicit goal. Every research engagement includes a transfer phase where findings are translated into integration blueprints, working code, or documented methodology that engineering teams can implement. Research that stays in a report has failed.
Have a hard problem?
Whether it needs autonomous experimentation, perceptual AI research, or a custom investigation — start with a conversation.