Vendacity: Empowering investment professionals with relevant data

Eight ambitious AI startups have been selected to compete in the AI Pitch Competition. The finals are on November 13, 2025, and IO+ will portray each contestant in the run-up to that event. The AI Pitch Competition is a Brabant-based contest that highlights the most innovative AI solutions, offering startups the opportunity to present their ideas, connect with industry leaders, and accelerate their growth. Today, we show what Vendacity has in store for the world. Founders Jasper Wennink and Rikke Schrauwen answer our questions.

What specific AI technology is at the core of your solution?

"We are using a multitude of AI technologies in our services. We aim to be a fundamental part of the dealflow structure which correctly and easily collects, enriches and stores dealflow data for venture capitalists. The current ‘AI stack’ is Azure-based and includes models for text-to-speech, Optical Character Recognition (OCR), and LLMs to collect data at scale. Moreover, we refine the data by using a variety of web and research agents along with conventional web scraping tools. Finally, we collect the data for our clients in a larger data format more readily available for analysis and plan statistical twins (synthetic data) on the longer term to provide analytical services for our customers that yield real insights and contain less noise than the current data providers. Our first versions of larger machine learning models that were used to predict startup and founder performance are a combination of Neural Networks (NN), XGboost, for smaller sample sizes, and reinforcement learning models. The largest difference between our service and that of other service providers is the full integration of services with large-scale data collection. Dealflow as a service is on the rise, while specific enrichment and selection is still left untouched."

How scalable is your AI solution?

"Our AI solution is primarily focused on the earlier stage of venture capital funds that want to manage their data intake of founders, startups, and are oriented towards a more data-driven investment model and decisions. Our solution can currently integrate emails, calls, CRMs, and other types of software, such as Harmonic AI or Pitchbook, via their API, which we are currently testing with our pilot partners. We provide tooling that can be used by any tech stack and a data model for each VC that provides insights over their deal flow, investment decisions, and key metrics for deal selection. Primary challenges are a safe environment for the sensitive data that we handle on a larger scale, and the correct protocols that come with it. Moreover, one of the key assumptions is the willingness of venture capitalists to contribute their data in anonymous form to create better models that can benefit the whole industry, which, on a more political level, is a sensitive topic."

How does your startup address potential ethical concerns related to bias, fairness, or transparency in AI decision-making?

"For our front-end users, the venture capitalists, we collect large amounts of personal information that relates to their dealflow funnel and should be handled with care and in line with GDPR, as is currently done in our demos. This data should be made unrecognizable before it will reach our core datasets without losing statistical value, this will be a hard task and is currently being worked on. However, the company data and non-personalized data are relatively flexible under the current GDPR rules, which enables us to store larger sets on the company level without issues from regulators."

In what ways do you believe your AI solution can positively impact society?

"We are a B2B data and AI company that provides investors with additional insights and collects public data from all sorts of current and new sources. Which, at their core, do not contribute to a better society other than our colleagues who have fewer frustrations, since they have a cleaner CRM and the ability to make better decisions.

The prediction models we envision would have societal impact where we aim to provide a unique understanding of the foundation of entrepreneurship in society and the selection criterion that can be derived from this. This would and should fuel academic discussion, innovation, and a different view of investing for early-stage founders, which is a much-needed revolution for the private market sector. Asset allocation would drastically improve, while the misjudgment of the models would constantly refine the model further in time. Knowing that such models create advice that should be ignored when you meet a founder who can defy all odds."

Tell us more about your entrepreneurial journey

"Our passion for venture capital and data began many years ago in a student house in Tilburg. Jasper has a background in investing and AI, and finished a bachelor's degree in AI last year. During his college years, he raised a student venture capital fund, Round One, and worked for DeeptechXL, a 100M+ deeptech venture capital fund. It is surprising that an industry with so much capital and a strong motivation for change would be subject to the current data quality issues and the lack of automation tools. Which is exactly what should change with Vendacity’s dealflow software.

For Rikke, the foundation of his interest in venture capital stems from his experience with multiple startups and his involvement in the Brabant startup ecosystem through incubators and a partner role at Round One Ventures. With a combination of finance, startup experience, and large-scale automation, the rise of AI has been of interest for a while. He wrote his master's thesis at the BOM, focusing on venture data quality and creating prediction models for startup failure and feature identification for startup success. Vendacity was the logical next step to set the next steps in creating a better data environment for venture. Together with Jasper, supporting earlier-stage VCs in their data transformation and data collection strategies.

Our hurdles are of a larger magnitude. Most of our target customers lack the foundational infrastructure needed for larger-scale data analysis, machine learning, and AI implementation. This is why we build a solution that can be wrapped around all sorts of tech stacks and captures the full front end of the funnel to secure better data and ensure our models can capture actual variance rather than the market noise. We have not overcome the large issues, these issues are systemic of our business and we work on them every day. Especially larger-scale collections and models are further ahead on our roadmap with significant challenges."

Regulation and Compliance: How are you preparing for the increasing regulatory frameworks around AI, such as GDPR, AI Act, or other data privacy laws?

"Our data collection tool and framework are aligned behind the Azure deployment in the European Union, in compliance with GDPR and the AI Act. Moreover, we store all sorts of personal data for internal usage and are restricted from selling this data to third parties by our collection and processing agreements. Compliance with respect to complete deletion from target individuals would be unique, but can be done in relation to the datasets of our clients.

However, removal of the synthetic data after generation would be impossible, but at this point the data has become an unrecognizable format. We understand this topic, together with cyber security issues, are the main concerns for our customers and society, and as individuals, we are critical of sharing any form of information on public domains."

Future vision: What is your long-term vision for your AI solution?

"Our AI solution will provide the entrepreneurial ecosystem with a hands-on, modular tool to connect all sorts of data sources from different dealflow pipelines and structure them in the needed tech stack, creating oversight and high-quality datasets for our clients. In addition, over time, we are building our analytics department to generate synthetic datasets for our clients' analytics teams, increasing understanding of entrepreneurship, improving asset allocation, and aiding entrepreneurs in determining the steps to take on their journey to success. In the first years we will focus on efficient collection, enrichment and execution with our customers, in 5 years we plan to have our first analytics and synthetic data sets available on a smaller scale for our clients, while in 10 years fund strategies and automation will have changed the venture capital industry forever."

The AI Pitch Competition: Why will you win this contest?

"Our vision is to empower both the investment professional and the next generation of innovators with better automation tools, easy-to-handle data collection, storage, and enrichment processes that can connect to any tech stack. At the same time, we aim to derisk investments in early-stage startups by providing better information services that will create greater opportunities for entrepreneurs and better access to capital for tomorrow's innovators. With the publicity and funding that the AI Pitch Competition provides, we would have the opportunity of a faster and more detailed rollout of our vision, together with our current pilot partners and the partner waiting list. The ability to speed up development by investing more hours in the startup and the much-needed flexibility that additional capital and connections provide."