Your company is expanding. Everyone's asking: which city next?
If you're the operator holding that question, you already have the spreadsheet — TAM, density, labor cost, permits, a column per metro and a winner by weighted average. Here's the problem: the spreadsheet ranks markets. It cannot rank what happens when you show up, because showing up is an event other actors respond to — and the responses are where expansions die. Timepoint simulates the responses. Here's how.
Timepoint AI is a Santa Monica company that runs decisions as grounded simulations: the specific, named actors around your decision, played forward through branching timelines. Findings come back ranked, with uncertainty stated in words — never sold as a prediction, always labeled as the AI simulation they are. Here's the full range of work →
Markets don't respond. Actors do.
The day you launch in a new metro, specific things happen: the incumbent's regional GM gets a target, the dominant platform's promo budget finds your customers, a city department opens a file, your anchor partner re-reads the exclusivity clause. Each is a named actor with documented incentives — which makes each one simulatable, per metro, before the capital commits. Three options and a deadline is the exact shape of multiple-choice analysis.
A ghost-kitchen network (fictional) has capital for exactly one new metro this year and three finalists. The spreadsheet had already picked its winner. The engagement's question was better: which choice survives contact with the people who get to respond?
| Rank | Metro | What the runs showed | Stated uncertainty |
|---|---|---|---|
| 1 | The spreadsheet's #2 | Promoted on response dynamics: the incumbent delivery platforms are mid-price-war there, and the simulated promo response to a new entrant was weakest — their budgets were pointed at each other. Regulatory branch: boring, in the good way. | Most stable across runs. Sensitive to the price war actually persisting two more quarters. |
| 2 | The spreadsheet's #1 | Best fundamentals, worst reception. In most runs the dominant platform's regional team — with the metro's best margins to defend — buried a new entrant in targeted promos for exactly as long as the entry budget lasted. | Runs disagreed only on how expensive; none made it cheap. |
| 3 | The wildcard metro | Cheapest entry, thinnest ceiling — and the regulatory branch carried a permitting timeline that swallowed the launch window in a third of runs. | The uncertainty IS the finding: this option is a coin-flip being priced as a bargain. |
Backward from the failure timelines in all three metros: the anchor kitchen partner. In the branches where the partner expanded on its own, the network's advantage evaporated regardless of metro choice. The spreadsheet had the partner as a constant. The simulation says it's the variable — and the contract conversation to have before picking any city at all.
The deliverable
A ranked entry brief: the response landscape per metro, the regulatory branch with its timeline spread, the assumption each ranking leans on, and the early indicator per city that says "this is the bad branch — adjust now." Re-runnable when the facts move, because they will. Scoped in one fifteen-minute call; runnable zero-knowledge if the expansion itself is still confidential.
See it in practice
- Six sample deal-team simulations — the same engine on rate scenarios, regulatory paths, and integrations
- A full engagement, worked end to end — one slot, three options — the multiple-choice shape, worked through
- The pivot-side version — when the question isn't where to go but whether to go at all
Field Notes on Deal Risk — an 8-page guide to stress-testing a decision across its branches: the five practices, a worked example, and the checklist. No gate, no sales call.