Research on early-stage investment decisions consistently finds that gut feel plays a larger role than formal analysis. Laura Huang's work at Harvard Business School, including a 2015 study in Administrative Science Quarterly and a 2014 paper in Academy of Management Journal, found that angel investors rely on intuition over structured evaluation, and that gut feel about the entrepreneur outweighs assessments of the business itself. The information available at this stage is genuinely thin, and the tools that exist haven't kept pace with the decisions they're meant to support. Teams rely on instinct and whoever has bandwidth that week.
Strong businesses get passed over when a pitch deck doesn't do the company justice. Investment decisions get made on the strength of a founder's narrative rather than what the analysis supports. Firms carry the same blind spots from fund to fund, year to year, because nothing in their process makes them visible.
Why gut feeling dominates
Venture capital is one of the few asset classes where information asymmetry is still significant. The data is thin, the timeframes are long, and most of what you're working with is hard to quantify.
But it also means decisions are hard to defend. Ask most investors why they passed on a company and you'll get a coherent story. Ask them what their hit rate looks like against companies they passed on for the same reasons, and they won't know. The data was never captured. The logic was never formalized.
There's a structural problem underneath this. VC returns follow a power law: the bulk of a fund's returns will come from one or two outlier investments. To have a reasonable probability of including one of those companies in a portfolio, a fund needs to evaluate a much larger universe of deals than most expect. In practice, most funds consistently underestimate what that volume of screening and due diligence actually requires from their team.
When reviewing a large number of applications, evaluation criteria start to drift. The first company gets a careful read. Later in the process, the bar has already shifted and nobody noticed. Ask what changed and the honest answer is: nothing was ever written down to begin with.
The process has no feedback loop and no memory.
Auryn is built to close that gap. A consistent process lets even lean investment teams handle the volume of deal flow a power-law portfolio requires, and ensures every investment was genuinely assessed for outlier potential.
What we built
Screening
Every application gets screened against your investment thesis automatically. The AI reads the pitch deck, extracts key metrics, and produces a structured alignment score with reasoning tied to criteria your team defines. The output is an evaluation you can act on, not a summary.
Structured analysis
Due diligence questions are standardized across your pipeline and pre-populated from available documents using AI, with confidence scores and source citations. The framework stays consistent regardless of who runs the analysis.
Exit scenario modeling
We calculate probability-weighted MOIC for every investment, with AI-assisted exit research pulling in comparable company data, market conditions, and your team's assessments. The output is a range of scenarios with explicit assumptions, not a single number pretending to see the future, but a structured view of the actual uncertainty.
Portfolio monitoring
KPI tracking, risk milestone management, and a live cap table reflecting every funding round and liquidity event. Changes in portfolio company performance surface in the platform as they happen.
We're in early access, working with a handful of investment teams to shape the roadmap. If you're running a VC firm and still managing deal flow in spreadsheets, get in touch.
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