What we do on image analysis platform field?

We provide image recognition software and cloud infrastructure for data analysis and storage.

The Oculyze Image analysis platform as used within all of our own products with hundreds of active users worldwide, is also available for partners to integrate their own image analysis pipeline.

Depending on your needs our Platform can be used along side existing imaging solutions or with new custom hardware. Via our API (Documentation available on request) you can also integrate any existing software already in use into our pipeline.

Already have software and need to move it to the cloud?

We can help you with that too.


Customized image analysis services

  • Step 1

    Take your images.

  • Step 2

    Automatically sent to the Oculyze cloud

  • Step 3

    Our customized image recognition software analyzes your microscopic images.

  • Step 4

    Your results are sent directly to you, giving you access from any internet-enabled device, a mobile app (custom development available), or get direct access to our API and create your own interface.

  • Step 5

    Generate customized reports, view historical data, track your results over time and increase statistical accuracy.


Let us help you digitalize the tedious and error prone manual analysis processes. Our image analysis technology combines methodical pattern recognition with artificial intelligence and deep learning, allowing us to develop more robust systems with less data than just raw deep learning alone.

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What are you currently spending?

If you use dry yeast for your beer you are probably paying around 0,15 € per gram. With a recommended dosage of between 50-80 g per hectoliter (hl) that adds up to around 10 € per hl you brew.

With just 1.500 hl p.a. production that amounts to 15.000 € per year!

Money saved through monitoring.

Monitoring your yeast will allow you to reuse your yeast up to 10 times. After a brewing batch you can asses your yeast viability and use it for the next batch. The second batch with the same yeast should actually be better than the first.

You will also be able to propagate your own strains, and use them to inoculate all of your brews.

What is the price of not monitoring?

Without yeast monitoring you will not be able to achieve consistent quality and you run a risk of disappointing your loyal customers!

Does image analysis automation make sense for me?

Wondering if you should automate your manual count? Use our calculator to get an estimate on how much you could save:



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This is your cost per day multiply by 260 labor days to get your yearly cost

The cost of an automated analysis is usually a 1/10 to 1/5 of manual cost.


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For many younger breweries doing proper yeast monitoring and management seems like a daunting task. However using the Oculyze better brewing technology, makes yeast analysis a simple task that will take you less than 5 minutes a day:

Other Systems:

  • Counting yeast cells manually requires training, is repetitive and boring.

  • Automation with yeast counters is expensive.

  • Yeast counters often require a separate determination of yeast viability.

  • Most yeast counters do not manage the result documentation for you.

Our System:

  • Is easy to use, with no special expertise required!

  • Is affordable – significantly cheaper than other yeast counters!

  • Measures yeast viability and concentration in less than one minute!

  • Automatically documents all results for you!

Industries we specialize in

– agriculture
– animal health
– biotech
– life sciences
– food & beverage
– digital health

Why it makes sense to run your AI built computer vision software in the cloud

AI Computer Vision Software

Using machine learning and Artificial Intelligence (AI), Oculyze has transferred the computer vision software for lab equipment from the table top to the cloud. We automate expert image analysis combining methodical pattern recognition with artificial intelligence (AI) and deep learning to create some of the best computer vision software money can buy. This base technology, used in the Oculyze yeast cell counters, Better Brewing and Fermentation Wine, has convinced hundreds of yeast labs, breweries and wineries of all sizes around the world.

By AI computer vision software we mean software that is able to do useful things, but without all the instructions being hard coded. Traditional software needs all possible cases to be taken into consideration during the initial programming. Since we mostly automate the analysis of images with a lot of variance, noise and biological diversity this feature comes in very handy as the software performs great on scenarios none of us has ever seen before and does so consistently.

Our first algorithms were specifically trained to count yeast cells in very challenging situations (high concentrations, in clusters and mixed with other particles). By watching thousands of these images and counting the cells in them over and over again the algorithms learned what is a live cell, a dead cell and how many cells are actually in a specific cluster. The algorithms were helped by traditional image pre-processing taken from traditional pattern recognition techniques.

Critics of this so called narrow AI say that it is actually artificial experience (AE) and not intelligence. It is estimated that it took the deep learning network “five” the equivalent of 45.000 years to beat humans in the game Dota 2. While this shows how much “experience” went into the intelligence of this network it also makes it easy to understand why this type of software is so superior for many tasks. When our hardware was successfully validated by the @vlb in 2016, the algorithms had been in training for less than one human year, yet the system performed as well as a professional with 20 years of experience.

Hard coded image recognition devices have been around since the 1950’s and combined the worst of two worlds- they were as expensive as the yearly salary of a human expert and were not able to learn from the samples they analyzed. Unless their software is later manually re-programmed the software stays the same forever. The price for these devices has dropped significantly in the last decade but until recently they were not able to gain any experience and the software did not improve over time.

In the Cloud

This changed when we started to use the cloud for other things than streaming music and storing pictures. The cloud gave us the cost efficient ability to use extremely high computing power by proxy on affordable handheld devices (<200 EUR/USD). Our computationally demanding algorithms would not be possible to run locally in a practical timeframe, but because the calculations are being performed in the cloud you have it instantly available on a handheld device.

As a result

Real beauty happens when you combine the two components, AI and the cloud, and gain a system that is flexible and affordable, using the experience from the many for the benefit of each individual (user). The samples from the devices allow the algorithms in the cloud to keep learning and improving the computer vision software for all users. This is how Oculyze computer vision software keeps getting better and better.

Some claim that a AI network can’t apply what it learned while playing Chess to play Go, reinforcing the argument that it is actually artificial experience and not artificial intelligence. While that may be true for different games, we have noticed that the experience gathered while counting yeast is helping our algorithms to count fibers, cylinders and other shapes better and quicker. This allows us to dramatically reduce the amount of images we need in our image analysis platform to train the initial algorithm for new applications.

In a time of shortage of skilled workers it makes a lot of sense to automate visual analysis tasks and reduce the reliance on human experience in favor of an artificial intelligence that stays within the company. It does not take 45.000 years to teach a human how to perform a manual visual analysis but it is impossible for a human to gain or access the combined experience available to cloud based computer vision software.

Combining Results

Since you have all your data already saved in a standardized format in the cloud, it becomes possible to also use AI for more advanced interpretations of the data. For wineries for example we help predict problems with fermentation by combining the results of various measurements, thus allowing our customers to react earlier and avoid problems before they even occur.

In summary: Cloud based, AI trained computer vision software is faster to develop and deploy, cheaper, more accurate and keeps improving over time.

What analysis are you thinking about automating? Send us a mail and include a picture of the sample.

Backed by years of research

Automatisierte Klassifizierung und Viabilitätsanalyse von Phytoplankton

Katja Schulze

Read more…

A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ

Katja Schulze, Diana A López, Ulrich M Tillich and Marcus Frohme

Read more…

PlanktoVision – an automated analysis system for the identification of phytoplankton

Katja Schulze, Ulrich M Tillich, Thomas Dandekar and Marcus Frohme

Read more…

The use of fluorescence microscopy and image analysis for rapid detection of non-producing revertant cells of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002

Katja Schulze, Imke Lang, Heike Enke, Diana Grohme, and Marcus Frohme

Read more…

FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms

– Kristin Verena Kaltdorf, Katja Schulze, Frederik Helmprobst, Philip Kollmannsberger, Thomas Dandekar , Christian Stigloher

Read more…

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