Investment-readiness signal
Find the seeds
that grow, in the
noise of every
inbound deal.
VentureSignal is your future investor-grade AI — it turns the public digital footprints of early-stage startups into objective investment-readiness scores, so VC teams can screen faster, fairer, and with greater efficiency and effectiveness.
30+
Dimensions
5
Signal clusters
0
KPIs required
Built at DTU Skylab · Copenhagen — onboarding the first Nordic partner funds
The screening problem
At seed stage, the data that ranks every later deal simply isn't there yet.
No financial data at seed stage
No revenue, no traction, no KPIs to rank on. First-pass screening defaults to gut feel — and gut feel carries every bias with it.
Hundreds of inbound deals a quarter
Analysts triage a flood of decks with no objective filter. The strong outliers and the weak ones look identical on the first read.
Network-driven sourcing has blind spots
When sourcing follows the warm intro, the best founder outside your network never reaches the top of the pile. Missed, not rejected.
How it works
Three steps from public footprint to comparable signal.
Aggregate
We gather the public digital footprint — company site, LinkedIn, media mentions, GitHub activity. Everything publicly available, enriched with our own dataset and incubator network.
Score
30+ dimensions across five clusters, each rated 1–100. Structured, weighted, and consistent from one startup to the next — tailored to your fund’s own theory.
Deliver
A comparable, VC-ready scorecard with clear red and green flags — so two startups can finally be read on the same axis.
Not a decision engine — a decision support layer. VentureSignal surfaces the right signal from the noise based on your specific needs. The conviction, and the call, stay with you.
The scoring framework
Thirty dimensions, grouped into five clusters of signal.
- 01 Strategy & Markets Problem clarity, market framing, differentiation.
- 02 Funding & Financial Signals Backing, runway signals, capital efficiency cues.
- 03 Team & Human Capital Founder–market fit, prior operating depth, hiring.
- 04 Digital Presence & Footprint Product surface, engineering trail, online maturity.
- 05 Media & Content Narrative, coverage, the signal the market sends back.
Not a generic model. VentureSignal runs on LLMs and machine learning under the hood — but tunes the dimensions, clustering, and weights to encode your fund's own theory. The score reflects how you invest, not a one-size-fits-all benchmark.
Where we start
Built for the Nordics first — then the rest of Europe.
The hard part isn’t reading a foreign startup. It’s that the details that separate a strong seed from an average one get lost at screening scale: weak signals, fragmented data, patterns that don’t surface outside your home market. VentureSignal makes them visible. Objectively, in any language, independent of network.
Weak signals, surfaced
The small details and faint patterns that separate a strong seed from an average one are exactly what gets lost at screening scale. We catch them.
Any language, fragmented data
European data is scattered and multilingual. VentureSignal reads it objectively — independent of language, context, or your network.
Nordic depth, then European reach
We start where the ecosystem is closest, to build depth and trust — then expand across Europe as the network grows.
Who we are
Born from research at ETH Zürich. Built at DTU Skylab.
The idea started from a research project Paolo completed at ETH Zürich, exploring how AI could evaluate startup quality from public data. We developed the idea into reality, brought it to DTU Skylab, and joined the startup programme’s Fintech Track.
We’re operators and builders, not just ideators — both currently writing our master’s theses at Novo Nordisk, building AI-driven optimization and digital-twin simulation for production scheduling.
Co-founders
Paolo Nalato
Data Science & AI
- Leads AI & digitalization strategy for a 200+ person department at Novo Nordisk Engineering — and has built 15+ advanced AI applications.
- Co-founder of an AI automation agency delivering ML, deep learning, and agentic LLM solutions to industrial businesses.
- ETH Zürich (Management, Technology & Economics) and DTU, with a mechanical-engineering background from Padua — where the original VentureSignal research began.
Matteo Pozzar
Business & Operations
- Digital consultant in healthcare strategic operations at Region Sjælland, building data-driven prediction models for clinical demand.
- Teaching assistant at DTU in Model-Based Machine Learning, Network Optimization, and Technology Innovation Management.
- Industrial Engineering & Management background, with consulting experience across Italy and Denmark.
The shared thread: we pair deep technical capability — machine learning, simulation, optimization — with hands-on business and operations experience. Turning messy, unstructured information into structured decisions is exactly what VentureSignal does for venture capital.
Early partners
We're onboarding our first partner funds in the Nordics.
If you run a European fund focused on early-stage startups, we want to hear from you. We're taking a small number of partners into the first cohort — and we read every request ourselves.