A live, paying platform with organic pull, priced to a single measurable proof.
Quinn is the interface a team uses to direct an AI workforce, and the signed rail every AI routes through to act on production. Ten teams pay for it today and brought in people we never marketed to. The seed funds the runway to prove the one number the whole thesis turns on: whether the shared cache saves across customers, not just within one.
Year 5 on the live model. Every tile recomputes off the same typed source the full model reads from. Flip the scenario and feel it move.
Disciplined concluded fair value today sits at $45.0M to $64.0M, held below the model on the one number still unproven: the cross-tenant reuse curve. The seed is priced to that gap.
Three things at once, and each one earns the next.
An intelligence that does real work
You talk to it where you already talk, it reaches almost any service you rely on, and it plans, acts, and verifies its own work against a quality bar before handing it back. That is the line between an intelligence and a brittle automation.
A brain that is yours to keep
Your memory, connected services, and the way it has learned to work for you live in a workspace provisioned exclusively for you, exportable at any time. The promise is exit, not capture.
A signed rail every AI routes through
Every action routes through one rail that verifies before it touches production. That rail is where the trust, the ledger, and the reuse live, and it is neutral by construction.
The proof is in production, not in a deck.
Ten teams pay for Quinn today, and they arrived by pull, not spend. The engine that makes the thesis compound already runs on real rows. We measure the moat instead of asserting it.
organic pull, no paid spend behind them; they arrived by referral and advocate unprompted
the metric the whole thesis rests on computes off real production rows today
close to covering its own costs at ten customers, so a raise is a choice, not a need
The downside is a profitable software company. The upside is a category network.
The three scenarios share the same starting point and the same cost discipline. They diverge only on the terminal cross-tenant cache-hit rate, and its downstream effect on margin and growth. The base case reaches EBITDA breakeven in Year 4.
Year 5 revenue: Conservative $24.1M, Base $59.4M, Aggressive $177.6M. The spread is the value of the one number, quantified. Source: live model, base anchors reconcile to the 12-tab sheet.
Three layers, each harder to copy than the one above it.
The everything-app surface
The messenger a team already lives in becomes the place they direct an AI workforce. No new tool to adopt. The wedge is the surface; the substrate is the company.
The cross-tenant cache
Work computed once is replayed for everyone at a fraction of a fresh run. A single model vendor cannot copy it, because a cache cannot span work it does not host. This is the one number the whole thesis turns on.
The participation economy
Builders publish actions and earn every time anyone runs one, while the buyer's cost falls. The saving and the payout come out of the same event. Supply compounds on its own.
$1.8M seed on a $35.0M post-money cap.
A Post-money SAFE for roughly 5% of the company, funding the runway to the reuse-curve gate: the milestone that turns the master assumption into a printed number. A later $10.0M Series A funds global scale once it prints.
Returns are indicative and refresh-required, computed off the live model. The seed is a bridge to the print; the print is what unlocks the A.