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Funding for digitalisation, IT and software development

Digital and IT teams are expected to deliver fast and build systems that last. What used to be enough, namely shipping features quickly, now has to hold up under regulatory requirements, scale economically under AI and data loads, and stand up to clear cost and impact criteria. A large share of this work can qualify for funding, but it is rarely recognised as such in day-to-day practice because it does not always look like classic research.

3D Spark
2R
Folienwelt
qlero
mm lab
Deutsche Firmenkredit Partner
ENTIAC
selecta one
DC Smarter
ZWF
Unternehmensgruppe Albert Weil
fierythings

The developments shaping your roadmap

AI has become infrastructure, not a feature. What matters is the foundation it requires: data pipelines, model integration, monitoring, governance and operational reliability.

The cost of digitalisation is a leadership topic. Cloud, compute, licences and energy demand influence scalability, margin and investment decisions, so teams must show efficiency as well as functionality.

Software and application development

New algorithms, significant improvements in performance, scalability or stability, and platform work that requires genuine experimentation, not just the implementation of known patterns.

Artificial intelligence and machine learning

The development or substantial adaptation of models and of the technical systems that make AI reliable, traceable and economically usable.

Data architecture and analytics

Real-time and high-volume architectures, privacy-preserving processing and analytics platforms that go beyond the capabilities of common standard tools.

Cybersecurity and resilience

Protection, detection and recovery methods whose effectiveness first has to be developed, tested and demonstrated.

Efficiency and sustainable IT

Development work on computing and energy efficiency where the effect cannot be derived from known methods.

The programmes behind it

Which instruments come into question for your project depends on the market. In Germany, digital and IT work usually falls under development funding, because there is no dedicated digitalisation programme. Depending on the project, the Forschungszulage, ZIM and thematic calls from kmu-innovativ or the IGP apply there.

Depending on the project, European instruments such as the EIC Accelerator and EUREKA Eurostars are added.

What to watch out for in this sector

In digital and IT projects, the funding potential often lies not in the subject itself but in the technical uncertainty behind it. This is exactly where most stumbling blocks arise in practice.

01

Routine instead of development

Many projects are described as software development but read in the application like implementation, configuration or integration of known solutions. A project only becomes eligible once it is clear which technical uncertainty had to be resolved and why standard tools or known patterns were not sufficient.

02

Too little delimitation of the development share

Digital projects often contain mixed activities: architecture, prototyping, testing, UX, data modelling, operations, maintenance and rollout run close together. If it is not cleanly separated which work is genuine development and which belongs to day-to-day business, the eligible share is frequently reduced.

03

Unclear evidence for development time

In software projects, working time is usually the most important cost block. Without traceable time recording, sprint documentation, tickets, work packages or technical decision records, it is difficult to evidence later who worked on which R&D element and when.

How we work

We do not start with the application but with your roadmap: which parts of your digital and IT projects contain real technical uncertainty, and which belong to normal delivery? Our specialists understand both the funding programmes and the evaluation practice in software, data and AI projects. Digital and AI-supported systems help us screen programmes more broadly, keep deadlines in view and keep evidence structured. The assessment of eligibility and the technical argument, however, remain with our experts.

Numbers you can hold us to

95%

Success rate

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Resources & Insights

Working with Ignite Group took a great deal of pressure off us. Thanks to […] their expertise, we always felt confident that our funding application was in the best hands.

Paula Hessing Senior Executive Assistant, MARKT-PILOT GmbH

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FAQs about funding advisory

View all FAQs

Yes, where the project carries technical uncertainty. What does not qualify is the pure application of known methods, for example a standard integration or configuration. Software development can qualify where new solution paths have to be tested and the outcome is still technically open at the outset.

Using existing AI models as such usually does not qualify. What can qualify, however, is the development work on the systems around them, for example where models are adapted, integrated, monitored or developed further so that they can run reliably, traceably and economically.

Yes, but eligible development work has to be delimited cleanly from the start. Even in agile projects you need traceable work packages, technical objectives and time records. If who worked on what is only reconstructed from sprints after the fact, the documentation quickly develops gaps.