Summary description: Startups struggle to secure funding because investors rely on fragmented metrics and inconsistent data, leading to poor matching and a persistent funding gap. In fact, only 2% of startups progress from Seed to Series A. VentureNerve transforms venture investing into a quantitative optimisation problem by integrating returns and probability of distress into a unified Risk-Adjusted Return framework that dynamically matches startups to investors based on personalised constraints. By combining structured financial standardisation, stress-tested modelling, portfolio optimisation, and AI-driven support, we improve matching quality, reduce information asymmetry, and enable more efficient capital allocation.
Problem: One key problem facing startups is access to funding. Startups find it difficult to be visible in a busy crowd. Investors struggle to find the right startups to suit their risk tolerance, sector profiles and portfolio diversification. Financials are difficult to analyse, connections hard to make, and support difficult to access.
Solution: Our solution, VentureNerve, tackles all three areas. To make financials easier, we calculate industry metrics, and alongside underlying advanced stochastic models, develop detailed insights into each startup. These include its risk profile based on recent data, as well as its sector and potential upside. These are displayed in a clear, aesthetic manner, allowing potential investors to easily research and compare startups.
To tackle connections, investors input their portfolio preferences. Our algorithm searches for the most suitable startups for them, alongside further metrics and stress tested scenarios, with a direct way to connect to founders. Alternatively, we also provide a portfolio optimisation tool to diversify the investor, underpinned by 10,000+ Monte Carlo simulations and crafted matching algorithms.
To provide support to startups and investors alike, we have also deployed a tuned AI model specialised to finance and startups. It uses the latest submitted data and can answer both specific queries about the startups as well as more general questions relating to specific metrics or industries, delivering an enhanced user experience. Safeguards are in place to ensure it can only answer relevant queries.
Impact: Our product delivers impact in all 3 areas.
In tackling financial analysis, investors spend less time scanning through multiple pitchbooks with differing formats and instead scan through a cohesive ‘marketplace’ with all key metrics automatically calculated. This also expands the visibility of startups, particularly to investors most interested in their sector or risk level and encouraging funding.
In making connections easier, our platform directly tackles the funding gap by allowing investors to review the most relevant and promising startups in their field of expertise. Our platform’s matching algorithm filters all startups, with underlying custom risk metrics and simulations to rank and connect the right investors. Easy to access, verified, contact details ensure that next steps are seamless.
We provide support to make funding startups simple. Our portfolio optimisation tool allows investors to view a range of diversified startups aligned with their risk profiles and preferences. Tailored support is also available through our customised chatbot, able to answer specific questions using live data. Examples include calculating and comparing different startup metrics, and general advice with constructing a portfolio in line with the investor’s strategy.
Overall, VentureNerve makes finding, connecting, and investing in startups simple and effective.
Challenges: Projections may be overly optimistic or based on limited historical data, leading to unreliable IRR and distress probability estimates. We address this by applying stress testing and Monte Carlo simulations to evaluate performance under adverse scenarios rather than relying on single-point forecasts.
Data provided by early-stage startups is often self-reported, unaudited, and inconsistent, which can distort risk and return estimates. We plan to mitigate this by enforcing structured financial templates and automated coherence checks to standardise and validate inputs at submission.
Log in or sign up for Devpost to join the conversation.