Deep technology (DT) startups develop physical products based on cutting-edge technologies to create entirely new markets. Consequently, they have a comparably high demand for specialized infrastructure, expert knowledge and extended development cycles which result in large capital expenditures. However, especially early-stage (pre-seed/seed) DT startups often fail to raise sufficient funding from investors due to their large capital needs, severe technical challenges often not fully understood by investors, and long time to market. Therefore, this paper analyses the underlying issues by developing a model to support early-stage DT startups by assessing their fit with different investor types (e.g., business angels, venture capital, or other investment opportunities) in order to streamline and focus their funding process. This is achieved by applying the principal-agent-framework to model the information asymmetry between different investor types and DT startups. Relevant signals between startups and investors are derived from literature, adapted to the requirements set by the signaling theory, as an approach to counteract the information asymmetry, and included into the model.