Turn facts into a testable investment view
Compose founder, technical, commercial, and network signals—then challenge the conclusion.
- 01Find signal
Combine ordinary facts into weighted observations.
- 02Model outcomes
Change assumptions and see expected value move.
- 03Challenge the thesis
Run bull and bear cases against the same data.
Combine weak signals into a testable conclusion.
See which facts contribute, how much they weigh, and what threshold changes the view.
No single row would survive a diligence call. The composition would change the valuation.
Founder alpha
A founder whose LinkedIn reads ordinary but who worked directly under the researcher who invented the underlying technology.
- Unusually strong prior collaborators
- Repeated success in hard environments
- Obscure research credentials
- Hidden network advantage
- Ability to recruit exceptional people
Technical alpha
Data showing that a moat is being built rather than described. Produces a technical-moat probability with citations attached.
- Patent filings and assignment records
- GitHub activity, package releases
- Benchmark deltas over time
- Unusual technical hiring
- Proprietary datasets, architecture shifts
Commercial alpha
Demand that shows up in other companies' behaviour before it shows up in a press release.
- Customer job posts naming the product
- Undocumented integrations
- Review velocity, traffic inflection
- Developer → enterprise expansion
- Procurement documents, reseller deals
Network alpha
Mediocre current numbers with extraordinary adjacency to the right people is a form of optionality that scoring models miss entirely.
- Founder and investor networks
- Advisor and early-customer networks
- University and former-employer graphs
- Syndicate and accelerator lineage
Classes light up as the observations above contribute to them.
Founders are scored on twelve axes, not one number.
Reconstruct the founder's trajectory across twelve dimensions instead of hiding it behind one score.
Forecast the next move. Score it later.
Timestamp the prediction and revisit it later, so research quality becomes measurable.
Founders build shift-scheduling tools inside a hospital system.
AI scheduling software sold to mid-size US health systems.
A large proprietary dataset of healthcare staffing behaviour across 40 states.
Labor optimization engine priced against overtime and agency spend.
A healthcare workforce operating system.
Worked trajectory for a healthcare staffing company. The same five-stage reconstruction runs for every tracked company.
If this succeeds spectacularly, what must become true?
Track each required condition separately, revise it as the world moves, and drag the assumptions to see the expected value change.
Amber conditions are company-specific execution bets. The rest are shared macro risks.
Force the thesis to argue both sides.
Both cases use the same data. The judge identifies the fact most likely to change the decision.
Kill thesis
- Patent ownership may overlap with a founder's former employer; assignment is not recorded with USPTO.
- 61% of revenueappears attributable to two customers, both in one vertical.
- Pricing may not support inference costs at scaleunder the observed usage mix.
- Competitor capability distance is asserted from public artifacts only; no private benchmark has been observed.
Bull thesis
- Hiring pattern suggests a transition into custom inference infrastructure, not model fine-tuning.
- unpublished but directly relevant prior IP held by the CTO, filed under a previous employer's assignment.
- independently hiring around the product — three early customers posting integration roles that name it explicitly.
- 12–18 months behind is where the nearest competitors sit on the public benchmark artifacts.
- The category is inevitable at scale regardless of whether this team wins it.
Both cases rest on the same data and reach opposite conclusions about what it funds. The conflict is resolvable from public record: patent ownership may overlap with a founder's former employer is checkable against filings today. Resolve that before the meeting — it moves the decision more than any other single fact.