What we deliver
AI strategy
We develop a digital roadmap for the provision of the solution in order to structure your approach to digital innovation. That strengthens your position in discussions with investors and is an internal team guidance.
Change management
Instead of setting and planning a tactical or strategic business goal, sales people need to collaborate with mathematicians to realize what customers are really interested in. Ideation not only defines the organizational structures of MVP development, but also the distribution of tasks in the changed company, which will lead to the business benefits actually being realized.
Automation
Already existing data can be used to get more control over all the related processes, to minimize the corresponding risk and to maximize the efficiency. Data-driven learning, automation and the generation of digital twins always play a crucial role.
Digital AI product development
We work with you to develop an innovative and technically realistic idea and turn it into a scalable product from start to finish based on a tailored product roadmap using all sorts of deep tech.
From ideation to MVP – based on data
We accompany our customers from USP detection and technical idea validation to event-storming workshops, generating prototypes and UI design.
We develop a digital roadmap for the provision of the solution in order to structure your approach to digital innovation. That strengthens your position in discussions with investors and is an internal team guidance.
We underpin your value proposition and your customer journey with digital information structures and interactions, design a solution architecture and integrate it into your value production chain. Depending on the size of the project, we can work independently or involve your team members.
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Ideation
The creativity in the ideation process lies less in the person of a designer than in the confrontation of the sales experience with the algorithmic possibilities. Hence, instead of setting and planning a tactical or strategic business goal, sales people need to collaborate with mathematicians to realize what customers are really interested in.
This phase is more important than any CI/CD process, because it not only defines the organizational structures of MVP development, but also the distribution of tasks in the changed company, which will lead to the business benefits actually being realized. Our team makes its experience from numerous German AI and IT start-ups available for this purpose.
- 2
Technological roadmap
We propose a complete technological stack oriented on scalability requirements as well as time and budget limitations, including recommendation of IT frameworks and mathematical approaches to the data-dependent problems that we have identified in the planned product during the ideation phase.
Each roadmap fulfills several functions: It is used for internal coordination, internal documentation and to establish a negotiating position with investors, who generally want to see a white paper in order to better assess the future prospects of a startup. Our team has a solid academic background and has already successfully raised seven-figure investments.
- 3
Rapid prototyping
Working in sprints and thereby meeting daily requirements to control our current working process, we fix issues as fast as we notice them, and make rapid phase changes according to the test results of our customers. This helps us collect evidence and test different new product step hypotheses in order to get traction early.
Rapid prototyping is a critical tool that allows experimentation with product modifications to establish a product-market fit based on empirical evidence, which is vital for any business. Our teams have years of experience with such methods in both SMEs and large corporations.
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MVP
Building an MVP takes 3–6 months and short sprints make progress quickly visible. User feedback is gathered directly. Major technology decisions are made and deep tech capabilities are used. Finally we hand over the MVP.
Flexible project management
Kickoff and discovery
We develop ideas, the roadmap, define the resources needed and get started.
Fixed price and time and materials
Even though we work in sprints, we remain open to scope adjustments to assure flexibility for our clients. Long-term engagements with teams larger than 5–6 engineers are typically time-and-materials based, after an initial fixed-price engagement period.
Sprint delivery cycles
Building an MVP takes 3–6 months and short sprints make progress quickly visible. User feedback is gathered directly.
Automation and algorithmic learning for digital use cases
Frequently, companies from various industries want to change or extend their business models, or they plan to open new business units, e.g. in other countries, based on their former experiences.
In all these cases, already existing data can be used to get more control over all the related processes, to minimize the corresponding risk and to maximize the efficiency. Data-driven learning, automation and the generation of digital twins always play a crucial role in all such contexts.
Here we present some examples of digital projects in which data-driven, totally tailored products have already been successfully developed in the past.
Internet of Things
The almost linearly increasing complexity of social and economic phenomena is a challenge and a chance for all of us. One of the chances emerges from the interconnection of embedded devices via the internet, enabling them to send and receive data, for that allows us to reduce the complexity of dealing with sets of objects of increasing complexity: And the automation of the IoT is the key to harvest all of these advantages.
Former projects
- Management and recording system for electricity consumption data.
- Data analysis pipeline with big data approaches for evaluating large volumes of sensor measurement data.
Computer vision and VR / AR
Under certain conditions the performance of the autonomous mind is not only constituted by internal factors, but also by factors external to the system: We sometimes off-load cognitive work onto the environment, making the environment hold or even manipulate information for us, and we harvest that information only on a need-to-know basis. And by using VR / AR we allow algorithms with their tremendous abilities to do the same with us.
Former projects
- Collaboration on and publishing of a VR action game, from conception to finished product, based on Unity3D – Space Station Loma Operations on Steam.
- Consulting and collaboration on various smaller VR projects, based on Unity3D and Unreal Engine.
Data science / predictive modeling
Extracting information from historical data using data mining methods allows us to generate quantitative insights into real-world problems, e.g. by model-driven regression or deep learning. Translating such insights, e.g. into decision tree models, minimizes future business risks and optimizes business outcomes.
Former projects
- Self-supervised clustering of market research data lying on a Likert scale that maps the empirical features of the customers to a purchase or a rejection of a purchase.
- Architecture, implementation and operation of a document management system with a special focus on accounting integration; here also text evaluation and automation of processes through machine learning.
Machine learning, NLP and AI
ML algorithms allow us to draw conclusions algorithmically from the insights provided by data science as a first step to more sophisticated technologies like generative AI or causal reasoning. These methods can be considered as enablers for a bunch of new types of business cases.
Former projects
- Generation of synthetic time series from one non-stationary, empirically measured realization of a dynamic process with preserved temporal dynamics.
- Detection of statistically independent partial flows in the cash flow of a company and their causal relationships that explain why and how a company actually makes money.
Explainable AI (XAI)
An algorithm that supports decisions has to be able to say why. That is what explainable AI stands for:
“I understand, why I understand why not, I know when you’ll succeed, I know when you’ll fail, I know when to trust you, I know why you made that mistake.”
Former projects
- The computed causal relationships between various categorical and non-categorical factors make it possible to identify the extent of the adjustments that have become necessary in all logistical processes. Case study: Planning & Scheduling