Aimondo AG: AI-flex, eCommerce optimisation – now as a cloud function platform

The pioneers for the incorporation of Artificial Intelligence (AI) and advanced algorithms for eCommerce have announced the imminent release of a complex functional platform for producers and suppliers. Freely definable data will soon be linked to the previous automated price optimisation for online trade and the industry. In this way, product planning can be created on a large scale from mass data and its analysis, with which future expectations can be predicted in a well-founded manner. Those who use this powerful technology will have decisive market advantages that can be implemented immediately by consumer goods manufacturers and distributors.

Heinrich Müller, founder and CEO of the Aimondo Group explains with a quote from Piaget[1] : “Intelligence is what you use when you don’t know what to do. Artificial Intelligence is no different. We generate product-related clues from data that are immediately used profitably. In addition, there are now scalable links to peripheral data that lead to different planning scenarios. They can follow patterns as well as contribute to new answers in the Piaget sense.”

If this seems too abstract, you can imagine that the Aimondo billions of data on products from all over the world, which have been collected and processed so far, as well as new data obtained every day, will be linked with parameters through the function of several other “artificial neurons”[2] . The overall picture obtained in this way is thus enriched and becomes a result. If additional training or real data is added to the system beyond the core functions, the Aimondo technology adjusts already generated values. As a result, new patterns can be recognised and the overall picture of result and prediction accuracy can be changed. In this way, the AI-flex platform gives Aimondo customers an unprecedented edge. They themselves can help determine how their individual “prediction machine” is made from the existing and daily expanded data quality through their own parameters or external data sources. In addition, the specifically adjustable target determination with its inherent exponential complexity prevents analyses and results from being the same as measures taken by competitors or even undesirable effects from arising[3] . Müller reminds us in this context that machines can deal with considerably larger amounts of information and more complex processes than humans[4].

In short, it is expected that the sum of past experience, the semi-permanent recording of the current situation and the influencing data will, by means of machine-accessible precision, result in a previously unknown level of success and planning reliability.

Alex Rose, who holds a doctorate in computer science, is one of the senior software engineers and architect of the new platform of the group’s Düsseldorf team. He explains it this way: “Additional information processed in the completely new user platform can change the overall picture. This is because new information can expand the understanding and perception of a situation or event. Depending on the nature and importance of the new information, it can reinforce, change or even refute the original picture. During programming, we sometimes found that even to us the results seemed surprising at first. This reminded me of a wisdom that Kierkegaard[5] described 200 years ago, that life is lived forwards but only understood backwards. We can now better simulate and thus understand this forward movement in important parts of the system. This helps to safeguard decision-making paths that are directed into the future. With every decision, this increases the degree of perfection that can be achieved. The algorithms specially developed for this purpose[6] are key to this.”

What is amazing is that an approach to achievable maturity is already being felt in many fields today. Entrepreneur Elon Musk, who has also been investing in AI for years, knows: “AI is capable of much more than anyone believes.” AI will also play a decisive role in the rapidly increasing online trade by providing competitive advantages. This already starts with the constant collection of data, business intelligence[7] .

Thomas Baierlein, a member of the Aimondo management team, picks up the beginning of the economic significance from the current hype about key technologies such as chatGPT or DALL-E and the diverse medical applications: “Microsoft boss Nadella is investing billions in AI, the Chinese state has been pumping ten-digit dollar amounts into AI for years. The importance is probably reaching that of the industrial revolutions and the Internet in general. At Aimondo, we consistently occupy a very small niche – structured optimisation in eCommerce and use elements of AI in particular as a tool in international data sourcing. To assess the meaning of “niche”, one should know that the global volume of eCommerce worldwide was about $3.4 trillion in 2022 and is estimated to exceed $6 trillion[8] by 2027. Even single-digit percentage movements are already absolutely significant figures. Our evaluations with the Aimondo technology used today, i.e. still without the AI-flex platform, have contributed to double-digit percentage improvements in results in online retail in terms of turnover, margin, exploitation of price elasticities, reduction of warehousing costs and inventory optimisation, campaign effectiveness in marketing. These are all hard facts. From the position of a technology leader, we are now – with some delay compared to the original plans – starting international marketing with customers and potential users. Through the dedicated cooperation with us, they save years of expensive in-house developments and can immediately use the AI-flex platform advantages as an edge over competitors.”

AI-flex is the trend-setting milestone of Aimondo’s new generation of automated quotation and process optimisation with a focus on consumer goods.

Aimondo investors, who recognised the signs as well as the potential of machine intelligence early on, will soon be given a first glimpse of the new Aimondo platform AI-flex on the threshold from alpha to beta version in Düsseldorf. With this big step into the future, the company is now also entering the phase of concrete preparation for public listing. This had already been planned since and for 2021. With the now expanded group structure under the umbrella of the Swiss Aimondo Aktiengesellschaft in Germany (Düsseldorf, Berlin), Great Britain (London), Italy (Bolzano), Switzerland (Zurich), Cyprus (Paphos) and Austria (Vienna), as well as the ambitious US plans, the time seems right to appropriately broaden the capital base, which for all new developments is as demanding at the beginning as it is promising in terms of returns later on.

The expectations of the industry in the United States regarding the future earning potential of pioneering companies becomes clear, for example, when one learns that currently, in hyped pure AI ventures such as Open AI, the foundation of Sam Altman and Elon Musk (in which Microsoft is now also invested to the tune of billions), the upper limit for the legally permissible financial returns of the formerly pure Non-Profit Organisation (NPO)[9] is quite high: OpenAI LP limits possible distributions to investors to one hundred times the investment. What goes beyond that ends up there in the pool of an NPO, which is supposed to remain free of the constraints on financial returns. So anyone who invests 100,000 dollars there, for example, will receive a maximum of 10 million.

Aimondo is of course a lot more cautious in its calculations, but shares many of the values and expectations that arise from the development of future AI capabilities. The combination with an NPO can possibly be realised as a US subsidiary in the flexible US market as a modern, sustainable form of enterprise. For AI-flex.



[2] Artificial neurons, in the sense applied here, are mathematical functions that receive input signals, weight them, sum them and then transmit them through an activation function to produce outputs from the sum of which new or modified outcomes and actions can emerge.

[3] Example: 19 October 1987 went down in history as the worst one-day crash of all time on the stock markets. The main reason for the crash, however, was that the first computer systems had started working in the eighties. At that time, they followed a simple logic: they sold papers immediately when they fell below certain price thresholds. This led to a vicious circle: the computers sold the papers, the traders also knocked their papers loose – which in turn led to the next wave of computer sales. This was an “undesired effect”.

[4] Pedro Domingos, author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Reshape Our World. He is professor emeritus of computer science and engineering at the University of Washington and a researcher in machine learning.

[5] Soren Aabye Kierkegaard (1813 – 1855) was a Danish philosopher, essayist and theologian.

[6] The term describes a sequence of instructions with which a certain task can be solved. The sequence of instructions is found in the software, is available in the source code and is configured browser-based in AI-flex.

[7] Business intelligence (BI) is a technology-based process for analysing data and providing actionable information that helps executives, managers and employees make informed business decisions. As part of the BI process, organisations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data, and create data visualisations, BI dashboards and reports to make the analysis results available to business users for operational decision-making and strategic planning. (Source:


[9] A non-profit organisation (NPO) is an organisation that is not for profit, but is dedicated to a specific purpose that is the goal of all income over and above what is earmarked for the operation of the organisation and distributions to investors. For this reason, non-profit organisations are granted tax-exempt status by the US federal government, which means that they do not have to pay income tax.

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