DATA MINING TECHNOLOGIES

Data-driven deep tech VC

Data Mining Technologies is a data-driven emerging
early-stage venture capital fund with bases in Limassol and London.


As ex-founders Alchemist alumni with scientific training
and backgrounds in finance and asset management,
we leverage our experience to support promising ventures in
disrupting industries and driving innovation forward.


Our investment approach focuses on providing
$20k-50k per deal, complemented by small opportunistic
angel-style investments and up to $1.5M syndicates.
We prioritize experienced founders, TOP5 accelerators alumni,
tackling challenging problems in FinTech and DeepTech.

Focus

GPU Audio
Zero Avia
Torus
Stackt
xRobotix
HyPoint
Jurby

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ML&AI revolution in VC


02 June, 2023 — According to global research, AI will be involved in 75% of venture capital investment decisions by 2025 up from less than 5% in 2021.

Using the potential of Data in the investment processes, VC firms can significantly improve their overall performance in a challenging market environment.

Today VCs leverage ML & AI technologies mostly for a deal sourcing through web crawlers and CRM integration systems. Increasing the efficiency of the deal sourcing, ML & AI enable VCs to analyze comprehensive databases and track high-potential startups, expanding their deal flow and increasing the chances of identifying promising investment opportunities.

While deal sourcing is commonly adopted, the development of ML models for portfolio support and due diligence is more challenging but holds significant value. VCs can enhance their decision-making by leveraging ML models to analyze and predict company performance and market dynamics.

ML models have shown promising results in predicting investment outcomes, such as exits, acquisitions, and IPOs. These models can achieve high accuracy rates, providing VCs with valuable insights for investment strategies.

Studies comparing the performance of ML models to actual investors have demonstrated the potential of AI in outperforming human investors. However, it’s important to note that top-tier experienced investors still outperformed the algorithm, highlighting the complementary role of human expertise in VC success.


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