
EIC Accelerator: Integrating AI Tools For Evaluations (Recommendation Series)
The EIC Accelerator funding (grant and equity, with blended financing option) by the European Commission (EC) and European Innovation Council (EIC) awards up to €2.5 million in grant and €10 million in equity financing per project (€12.5 million total) and is designed for startups and Small- and Medium-Sized Enterprises (SME), often supported by professional writers, freelancers or consultants.
This article is part of a series that contains suggestions for the EIC and the EIC Board regarding various improvements to the evaluation process (see ChatEIC).
AI evaluations: It’s best to start early
It is best to start developing a process now since there will need to be an experimentation phase. It is better to have it fully developed when needed than to be forced to integrate it on short notice in case submissions keep rising due to AI. Many applicants already use AI instead of completing the template themselves (i.e., letting ChatGPT fill it out), and consultancies are actively considering how AI can be used to speed up the EIC Accelerator process. It is only a matter of time before proposal writing becomes fully automated.
Experimentation
Several approaches have been outlined (see Part 1, 2, 3, 4), but an experimentation period is necessary to determine what works best. One possible approach is to use multiple AIs per criterion and employ multiple extraction steps before scoring. All past proposals and approvals can be used for training as well. Criteria should also be revised to give weight to what the jury cares most about so that the AI knows the real-life priorities.
Why AI is needed
The EIC has been increasing Jury numbers over the last few years. With the success rate for the interviews dropping, this suggests it is not the ideal scenario. The most important KPI for the evaluation process as a whole is the interview success rate, as it shows how many of the unwanted projects slipped through Steps 1 and 2. The higher the interview success rate, the better the evaluation process is at filtering. Since success rates keep falling for every cutoff (from 50% in 2021 to 16% in 2025), the evaluation process needs to improve.
Setup
It is recommended to use a European company that runs an open-source Large Language Model (LLM) locally. Sending all Intellectual Property (IP) to the US/China would not be ideal (even if confidential), but Europe does not have many LLMs to choose from. Taking the best open-source LLM(s) on the market and then running them securely on EU servers is the ideal approach. Then, one can use a simple setup for the AI system that separates the backend/UI and the instructions themselves.
For instance, ChatEIC was created for proposal writing using a similar approach. All the "magic" is saved in the instructions as plain text. To change the output, the individual instructions are simply changed. Using the same approach, feedback can also be quickly integrated into the system, and different types of evaluation instructions can even be run in parallel to test them. The benefit is that the EIC and EIC Board can retain control over the evaluation while the contractor is exchangeable since they only take care of integration and version control.
The software backend (not the proposals or data) can even use version control via GitHub or similar (but still a private repository), since the software itself is not that sensitive, making it very easy to change providers if needed. This approach can entirely avoid the problem and dependency that occurred with the old AI platform.
Benefits
Infinite evaluators can evaluate instantly. Instead of having 3-4 evaluators work for 2 months, an AI system can cycle through dozens of repeat evaluations to perfect the outcome. Feedback from the board and jury can be used to noticeably refine the Step 1 and 2 filtering. The goal is to increase the Step 3 success rate (the most important KPI) while decreasing the Step 1 (and 2) rate(s).
There is also a perfect record of evaluation decisions. As a result, any “poor” project that ended up in the interview can be traced through Step 1 and 2 to improve the instructions. Any great project that ended up winning but was previously rejected can likewise improve the instructions to make sure such a project is not rejected again.
Integration
Only when the process demonstrates higher quality than the human evaluators should it be integrated. If the results are promising and create impressive outcomes, some top AI picks that were rejected by the human evaluators could also be invited to see if the Jury likes them (as early testing).
Long term
There are many possibilities. Once the process works well and is either a part of or becomes the main evaluation system, the AI can be extended to engage applicants. If information is missing or unclear, it can auto-send questions to applicants to which they can add additional clarification (i.e., a real-time information exchange). The template can also be replaced by a very short cheat sheet (i.e., condensed information) followed by pure AI Q&A. Experience in creating AI writers and writing proposals suggests that a large part of proposals consists of unnecessary content ('bloat'). This could be streamlined.
Feasibility
No major roadblocks are foreseen in the integration. The context window should be able to handle 100+ page proposals well (ChatEIC already demonstrates this capability). Image recognition could be discussed more, including where and if it is necessary. Connecting the AI to patent databases and allowing web searches would also be useful, mostly for market, competitors, science, and IP analysis.
It could also be possible to use a hybrid approach where evaluators use the AI outputs as a dashboard that points them to the pros and cons of the project. Then the AI could act like the initial 3 evaluators, and the human evaluator could be the 4th who makes the final decision. An AI system is believed to have the potential to improve filtering, enhance interview success rates, and, most importantly, increase the quality of winners. Also, the EIC can make the shift from mostly grading proposals like essays to becoming more of a scout that finds the best companies.
Currently, applicants have to learn about the EIC and what it wants, but in the future, the EIC might have a system where no learning is required. The EIC could then select from all European companies, not only the ones that decide to take the time to learn about the EIC first. As long as the EIC and companies are aligned, removing the learning barrier would remove friction in connecting with them.
These tips are not only useful for European startups, professional writers, consultants and Small and Medium-Sized Enterprises (SME) but are generally recommended when writing a business plan or investor documents.
Deadlines: Post-Horizon 2020, the EIC Accelerator accepts Step 1 submissions now while the deadlines for the full applications (Step 2) under Horizon Europe are listed below. The Step 1 applications must be submitted weeks in advance of Step 2. The next EIC Accelerator cut-off for Step 2 (full proposal) can be found here. After Brexit, UK companies can still apply to the EIC Accelerator under Horizon Europe albeit with non-dilutive grant applications only - thereby excluding equity-financing. Switzerland has resumed its participation in Horizon Europe and is now eligible for the EIC Accelerator.
EIC Accelerator Step 1 Deadline 2025
Contact: You can reach out to us via this contact form to work with a professional consultant.
AI Grant Writer: ChatEIC is a fully automated EIC Accelerator grant proposal writer: Get it here.
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