How Does the Forschungszulage Work for AI Companies?
TL;DR – Summary
If you as an AI company are working on a technically demanding project (e.g. new model architecture, GDPR-compliant agents, robust data pipelines, novel automation), the Forschungszulage can be one of the most attractive financing options: retroactive, non-repayable and suitable even for startups without profit. The key is that your project is truly innovative and meets the criteria of novelty, technical risk and systematic approach.
Why This Article Matters (and What AI Teams Need to Understand)
Many AI teams invest heavily in personnel, data preparation, training, testing and product integration early on – often long before significant revenue flows in. This is exactly where the Forschungszulage comes in: it can reduce the financing burden without having to rely on classic, highly competitive grant programs with tight deadlines.
Important to understand: it is not about using "just any AI" – but about demonstrating real innovation that goes beyond the state of the art.
Briefly Explained: What Is the Forschungszulage?
The Forschungszulage is a tax-based funding scheme for companies that implement innovative development projects (within the meaning of the Forschungszulagengesetz). For AI companies this often involves software, data and modelling projects – the decisive factor is not the industry, however, but whether your project meets the criteria (e.g. novelty, technical risk/uncertainty, systematic approach).
Particularly relevant for AI companies:
- Even SMEs and AI startups without profit (and sometimes without revenue) can benefit: the Forschungszulage is credited via the tax assessment and can effectively result in a cash payout.
- The funding can be used retroactively (depending on the period and tax implementation) – and it can also be applied for projects that are already underway or completed.
- You receive non-repayable relief – typically in two stages: first the BSFZ certificate, then the assessment by the tax office.
Which AI Projects Are Generally Eligible?
In principle, AI, IT and software projects are frequently eligible – if they qualify as innovative development work (e.g. experimental development).
Typical AI examples that often qualify:
- Development of reliable, GDPR-compliant AI agents/chatbots (e.g. for internal processes, customer service, document workflows)
- New methods for retrieval, RAG quality assurance, hallucination reduction, guardrails, evaluation
- Technically novel approaches to data preparation, labelling, synthetic data, privacy-by-design
- MLOps innovations: robust deployments, monitoring, drift detection, secure model updates
- Specialised models/algorithms for industrial, medical, legal or finance applications with demonstrable technical hurdles
Practical example: An AI startup developed a chatbot that processes emails and documents, automates filing and adapts to processes – the innovative development work was funded (with a focus on reliability/GDPR compliance, among other things). The feedback: high impact with comparatively low "bureaucracy feel" compared to other programmes.
The 3 Central Criteria: How Your AI Project Becomes "Eligible"
For your project to be considered eligible, it must – in addition to being classified as development work – primarily meet these three criteria:
1) Novelty: What Is New About Your Approach?
Your project must generate new knowledge/capabilities or use existing ones in a way that produces substantially better products/processes/services.
Important: it may not be sufficient to "merely" use an existing tool and train a model with it. The decisive factor is the technical innovation at the core of your project.
2) Technical Risk / Uncertainty: What Could Fail – Technically?
Reviewers want to see that there are genuine technical hurdles that could jeopardise success (not just commercial risks). Examples:
- Stability/robustness in edge cases
- Data protection requirements that standard solutions do not meet
- Quality targets (e.g. error rates) that are not achievable with "standard RAG"
- Scaling/performance under real-world conditions
Practical note: follow-up questions are normal. In one approved case, for example, the technical risks and why they could not be resolved with existing technology were requested in more detail after submission – and then successfully answered.
3) Systematic Approach: How Do You Proceed in a Structured Way?
Concrete examples of how a systematic approach is documented in development projects can be found here: Example projects.
You need a traceable project logic:
- Work packages, milestones, intermediate results
- Resource and personnel planning
- Tests/evaluation (measurable metrics!), documentation
Why the Forschungszulage Is Often "the Best Option" for AI Companies
For many AI companies the Forschungszulage is so attractive because it:
- Applies retroactively (innovation is often already "in progress" before anyone thinks about funding)
- Does not dilute (no equity trade-off like with VC)
- Can be predictable (with clean project documentation)
- Has a strong effect especially with personnel-intensive development (data/ML/engineering)
How to Proceed: Forschungszulage in 3 Steps (AI-Focused)
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Check project fit (innovation rather than buzzwords)
- What is concretely new?
- What technical uncertainties exist?
- What measurable goals/evaluations are in place?
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Describe and document the project clearly
- State of the art vs. your own advancement
- Technical risks and solution approaches
- Plan, milestones, artefacts (repositories, test protocols, model cards, documentation)
-
Implement correctly and map the payout via tax
- After a successful review the allowance takes effect via the tax office (in practice many avoidable mistakes happen here).
Why Working with dieforschungszulage.de Is Worthwhile (with Clear Advantages)
If you want to use the Forschungszulage strategically, one thing above all counts: the right argumentation of your innovation – so that it is traceable, verifiable and consistent.
With dieforschungszulage.de you get:
- Free initial review of whether your AI project is a good fit in principle (quick clarity instead of months of guessing)
- A clearly structured process for the project description (novelty, risk, systematic approach)
- Support so that you don't leave any eligible costs on the table and can answer follow-up questions cleanly
Conclusion: Financing AI Innovation – Without Detours
If your AI project is more than "implementing standard tools" and involves genuine technical innovation with risks and a structured approach, the Forschungszulage is one of the strongest levers for financing development – especially for SMEs and startups.