Start a Decision Case →
🐾3Dogs NexusStructured Decision Intelligence
Case study · economic-development decision support

Before a city spends incentive dollars on a grocery store: the 12-model AI panel said test operator interest first.

By Alan Finney — Founder, 3Dogs Nexus

A mid-size city wants a full-service grocer in an underserved district. The reflex move is commissioning a $50,000–$75,000 market feasibility study before committing incentives. This demonstration run put that exact decision through 3Dogs Nexus — and the panel pushed back on the reflex, recommending a cheaper partner-first sequence that tests real operator appetite before any big study is funded.

156 API calls · 12 AI models11-analyst panel: 11–0 proceed-with-conditions2m 47s end to endModerate confidence · dissent-preserving

The call, verbatim

The panel didn't rubber-stamp the feasibility study — it reordered the sequence so the city spends the big money only after real grocery operators show real interest.

The two-phase plan the panel specified

Phase 1 — city-funded baseline ($15K–$25K)

A trade-area viability assessment: demographics, income, competitor leakage, and minimum demand thresholds. Cheap, fast, and it either kills the idea early or arms the city for real operator conversations.

Phase 2 — joint study with committed operators

A feasibility study co-invested with shortlisted operators, scoped to their parameters — lease costs, capex, ROI hurdles — with third-party validation of operator projections.

The dominant risk it named: operator-appetite uncertainty. If fewer than three operators commit in writing, a $50K–$75K standalone study becomes a sunk cost with no path to an actual grocer — leaving the city to either abandon the project or overpay subsidies to attract a single bidder.

What the evidence showed

The panel grounded the recommendation in comparable mid-size cities — including Edenton and Waco — that built successful downtown grocery projects by validating demand and operator interest in stages rather than commissioning a monolithic study first. The district's demographics support a store; the open question was never demand on paper, it was a committed operator.

How does AI market-feasibility analysis help an economic-development office?

The same way it helps any decision-maker facing an expensive default: it stress-tests the sequence, not just the answer. Here the adversarial panel's value wasn't “yes or no on the grocery store” — it was restructuring when each dollar gets committed, with named conditions and a documented rationale an economic-development office can put in front of a city council.

The delivered report

The complete recommendation as delivered — the call, the conditions, the panel vote, the risk analysis.

Download the full PDF report →