Lab Productivity AI

Our lab-integrated AI software assists chemistry and materials teams in optimizing formulations, processes and experiments. Our AI constantly generates knowledge with each experiment, so you avoid dead ends and bring innovations to market faster.

AI Enterprise Software | AI Strategy Consulting

See How It Works ↓

R&D will always be challenging. But much of the friction is avoidable.

Chemistry and materials R&D is inherently complex. But too often, the hardest part isn’t the science itself. It’s the way work is organized around it.

Trial-and-error does not scale

Your team runs hundreds of experiments a year. In complex, multi-variable problems — formulations, catalysis, process optimization — even experienced researchers can spend months exploring without converging. Not because they lack skill, but because the search space is simply too large for intuition alone.

Knowledge gets lost between teams

Bench optimizes a formulation. Pilot re-optimizes it under different conditions. Plant starts over again. Each group runs experiments that could have been informed by the previous stage — but context, assumptions, and results don’t travel.

AI is often a solution looking for a problem

Many AI tools push technology because it sounds modern, not because it helps scientists. The result: scepticism from experienced researchers, low adoption, and minimal impact. AI only creates value when it improves experimental decisions. Otherwise, it’s noise.

busy scientist

Structure your R&D

Define objectives, map pipelines, and connect experiments to outcomes. Every experiment has a purpose.

Run smarter experiments

AI suggests what to try next based on what you’ve already learned. Fewer experiments, faster convergence.

Compound your knowledge

Learning accumulates across experiments, teams, and scales. No more starting from scratch at every handover.

Step 1: Define your project

Map your experimental workflow, set your parameter space, and define what success looks like. Takes less than an hour.

Step 2: Run experiments

Edison suggests an intelligently selected first batch. You run them in your lab, as you normally would. Upload the results.

Step 3: Learn and iterate

Edison learns from every result and proposes smarter next experiments. Each cycle gets you closer to your objective. What used to take 100 experiments often takes 20–30.

Step 4: Scale and share

Knowledge travels with the data, from bench to pilot to plant, from one team to the next. No more reinventing at every handover.

70–80%

fewer experiments to reach targets

2 weeks

to breakthrough (vs. months of trial-and-error)

€75,000+

saved in researcher time on a single project

From disconnected labs to a Knowledge Engine.

Most R&D organizations don’t have a data problem. They have a knowledge problem. Experiments happen in isolation. Results live in notebooks and spreadsheets. When someone leaves, the knowledge walks out with them.
Aixelo turns your R&D into a compounding knowledge system. Every experiment, every pipeline, every project contributes to a growing body of evidence about how your systems actually work. Over time, this becomes a genuine competitive advantage: your organization doesn’t just have data — it has understanding.

Disconnected Labs

Isolated experiments. Knowledge resets at every handover. Teams repeat each other’s work.

→ Connected Pipelines

Shared objectives, shared data. Learning flows bench → pilot → plant. Context travels with the experiments.

→ Knowledge Engine

Every experiment compounds. AI-assisted decisions. Fewer wasted experiments, faster convergence, higher confidence.

OPEL Framework

A pragmatic methodology for structuring R&D: define objectives, map pipelines, run coordinated experiments, and capture learning.

Learn more about OPEL

Edison 4.0

Web-based platform for adaptive experimentation. Define your workflow, set objectives, and let AI guide your experiments.

Explore Edison 4.0

Lab Productivity AI

R&D will always be challenging. But much of the friction is avoidable.

A better way to run R&D.

Structure your R&D

Run smarter experiments

Compound your knowledge

From first experiment to compounding knowledge.

Step 1: Define your project

Step 2: Run experiments

Step 3: Learn and iterate

Step 4: Scale and share

Results that speak for themselves.

70–80%

2 weeks

€75,000+

From disconnected labs to a Knowledge Engine.

Disconnected Labs

→ Connected Pipelines

→ Knowledge Engine

The ultimate goal is not efficiency — it’s compounding knowledge. Your organization doesn’t just run experiments. It learns.

"OPEL is how you structure AI-driven R&D. Edison is the software tool where you do it."

OPEL Framework

Edison 4.0

Ready to make every experiment count?

Our Team

Dr. Christoph Kreisbeck

Co-Founder

PD Dr. Manuel Tsotsalas

Co-Founder

Prof. Dr. Pascal Friederich

Co-Founder