An overview of our references

STARTING POINT

TagEnergy develops, builds and operates climate-friendly power plants and energy storage facilities. With the help of innovative software, the company aims to offer energy at an optimal price/cost/environmental impact ratio.

New simulation software will be developed by an experienced solution provider based in the US, to enable TagEnergy to better understand and improve the performance of its assets in the market. The mid-term plan is to use the collected market and operational data to develop suitable AI models for trading insight.

CHALLENGE

Before TagEnergy enters into a long-term collaboration with their partner, Cape of Good Code is to carry out a supplier qualification to check whether their software technology and development processes are future-proof, the  knowledge of software optimisation and AI technologies is available and whether the new software will be a sustainable long term solution for TagEnergy.

For reasons of IP protection, our DETANGLE® software analysis toolset could not be used to assess the quality of the architecture.

RESULT

Customised interviews were conducted with relevant partner stakeholders instead. For additional information, the partner agreed to apply several analysis tools to its existing software solutions. The results were automatically analysed and correlated with the information from the interviews. This made comprehensible assessments of code and architecture quality, AI readiness, risks associated with the use of open source and the quality of the development processes possible.

The findings and conclusions were visualised and summarised in the final report showing that the potential partner fulfills the criteria and benchmarks required by TagEnergy.

STARTING POINT

Indivumed (Hamburg) is involved in a software provider in Greece and uses its ‘Automated Machine Learning’ tool to validate and optimise its own AI to derive individual cancer therapies. The aim is to investigate whether the Auto-ML software and development processes are suitable as a basis for their application in other industries with extended or new requirements. 

CHALLENGE

In order to assess the potential of the Auto-ML software, it was scanned remotely with DETANGLE to assess the current status of the architecture and code quality. In addition, the feasibility of the requirements from other industries and the required effort are to be investigated together with the team on site. The risk of dependencies on individual developers has to be analysed and verified on site with interviews.

RESULT

The DETANGLE data shows that feature development is now efficient and that technical debt is being structurally reduced. They also show that there are strong dependencies on individual developers and that some development process steps need to be improved. The software architecture proves to be sufficiently flexible for further features and new applications in new domains.

STARTING POINT

Siemens plans to acquire a leading international provider of innovative digital services for its Mobility Group. 

CHALLENGE

Software is the key value driver of the planned acquisition. The future viability of the software is largely determined by its architecture. 
Cape of Good Code measured the technical debt of the software architecture with its DETANGLE analysis suite. Of additional interest was the proof of an appropriate knowledge distribution and an adequate development efficiency. 

RESULT

The technical debt in the software architecture was localized and evaluated by a DETANGLE scan. The effort required for the necessary architecture optimization was determined from the analysis data and was reasonable. There was also no evidence of red flags in development efficiency, knowledge distribution and possible dependencies in the development organization.

STARTING POINT

Atotech acquired the software company Visutech Plating, which develops the automation control software for Atotech plating lines.

CHALLENGE

Analysis and evaluation of the quality, scalability as well as the technical sustainability of the code and architecture of the software by considering the background that only some of the developers will be part of the transaction.

RESULT

Exclusion of Red Flags at a very early stage of due diligence. Furthermore, all risks could be made transparent and priced into the acquisition.

STARTING POINT

Pinova Capital is considering an investment in a German software company with outsourced development at an Eastern European development service provider.

CHALLENGE

The software provider for sale had the software realised by an Eastern European development service provider. In the red flag due diligence, the technical cooperation of both companies and potential dependencies were to be examined in particular.

RESULT

Rapid risk assessment of the organisation and processes for development and maintenance, so that a detailed analysis of the software and architecture could be carried out. A mismatch with Pinova Capital's cloud strategy was identified at an early stage.

STARTING POINT

Checking the quality of the software technology before launching a digital platform for passengers.

CHALLENGE

Using the DETANGLE Analysis Suite, important KPIs for the identification and localisation of Technical Debt, but also the structural deficits in the development process could be determined directly from the development history of the code. The management was thus able to implement important adjustments with existing resources before market launch without jeopardising the schedule.

RESULT

The problematic code areas could be correlated with the respective functionalities and a cost-benefit analysis between the effort to fix the technical debt and the business value of enhancements of existing and planned functionalities by the client could be performed.

STARTING POINT

A very young start-up has developed a video app and, after the successful proof of concept, is looking for further investors to invest in marketing and further features. For this, it is necessary that the software technology at hand is suitable as a basis for the targeted growth.

CHALLENGE

Validation of the architecture scalability for 1000 times the number of users based on a proof-of-concept of the server side.

There was only a small time window and a tight budget available.

RESULT

Data-based suggestions for the necessary adaptation of the software architecture so that the app software functions reliably even with very high user numbers and can also be further developed. In addition, optimisation of the engineering process with best practices and state-of-the-art development methods.