Where to open the next club. Where to expand retail. Where to relocate the team. Where your clients should buy. The scoring engine behind Caterelo's consumer product — 90 regions, 13 signals, quarterly-updated official data — is config-driven. Your dataset + your brand = your decision product, in weeks not quarters.
See the 2-minute proof (FitMap demo) → Live consumer productStart small, scale to white-label. All tiers run on the same production engine and data pipeline.
No rebuild per client. A vertical is a JSON file; the engine doesn't know if it's scoring regions for relocation or districts for gym expansion.
Your units (districts, cities, stores), your metrics, your definition of a "good" location. We map it to the config schema.
Palette, logo, labels, scoring weights, verdict copy — one file. Engine renders your product on a staging URL.
Your team makes one real decision with it. We calibrate weights against what your operators already know.
Quarterly refresh, new units, new signals. You own the decisions; we own the pipeline.
// engine.config.json — a complete vertical
{
"brand": { "name": "FitMap", "palette": "#FF7A1A", "unitLabel": "district" },
"dataset": [ { "id": "moko", "name": "Mokotów", "sat": 62, "pop": 4100, … } × N ],
"metrics": [ { "id": "sat", "label": "Saturation", "dir": "min" }, … ],
"weights": { "sat": -0.35, "pop": 0.30, "income": 0.20, "rent": -0.10 },
"verdict": "rules → plain-language recommendation"
}
Anyone whose money depends on a location decision made with spreadsheets and gut feel today.
Saturation scoring per district: where demand outruns supply before your competitor sees it. (See FitMap demo.)
Expansion pipeline ranked by footfall, income, rent and cannibalization risk — one dashboard for the board.
Where to relocate teams: cost, safety, schools, visas — the consumer engine already does exactly this.
Embedded "which region fits you" widget = qualified leads with a complete client profile attached.
Region intelligence layer on your listings: LifeTrend scores, climate 2050, yield benchmarks via API.
Your copilot answers location questions with live scored data via MCP instead of hallucinating from training data.
If the engine doesn't make your next location decision measurably clearer, you keep the analysis and walk away.
Book a pilot call → hello@r352.com