A demand platform already sees what its users do. Living Lab adds what the city has coming: the events, the flights, the hotel curve, and the sentiment in each market, so the platform sees demand form before it arrives.
One model of everything that moves demand in a city: every event on the calendar, every inbound route and its seat capacity by week, the hotel booking curve, paydays and holidays in each origin market, weather, and the search interest and social sentiment there. Signal137 names the cause when your own signal spikes. Demand137 delivers the forward layer weeks ahead, as a feed your pricing and supply models can consume.
Each spike in your signal is traced to its cause, whether an event, a route change, a campaign or a market shift, and attributed with synthetic controls. Which of your cities and weeks were moved by which events. What a past event did to rides, rooms, or bookings by hour and by district. Every number marked observed, derived, or inferred, delivered as data, not as a report.
The forward demand layer, weeks before the booking curve moves. Where the audience will stay, how they will move, and when they will arrive, by district and by hour. The access ceiling stated, so the forecast never exceeds what the routes and rooms can carry. The interval stated, the model version stamped, and a refusal when the inputs cannot support an answer.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to build a demand-forecasting layer in-house or integrate one | A fair comparison of build cost and time against what a connected signal already adds | The fastest defensible path to a differentiated product |
| Demand137 | What connected context adds beyond our own data | The driver and duration of a demand step-change, and whether supply can meet it | A platform that explains demand before the customer notices it moved |
| Demand137 | How do we explain a demand shift to customers with confidence? | An explainable, attributable method rather than an opaque score | A demand answer customers can act on and justify to their board |
| Demand137 | Can this run as an API or a feed, not a report? | A licensed feed, an API, or a co-developed model built to your specification | Integration into your product, on your timetable |
| Demand137 | What does a proof of concept look like? | One market or signal type, connected once, so you see what it adds | Your team assesses the value before committing to a wider integration |
| Signal137 | Why did that market move last quarter? | The event, route, or price driver attributed, with the counterfactual stated | The rear-view explanation your customers can quote |
| Signal137 | Which of our customers' campaigns actually moved demand? | Attribution of demand shift to campaign, net of what would have happened anyway | A story your destination customers can defend to a funder |
| Both | Does a partnership strengthen or dilute our own data asset? | Clear terms on ownership, governance, and exclusivity, set before anything moves | Capability added without ceding the asset |
| Both | Are you building a competing consumer-facing platform? | No. Living Lab stays as a data and method layer | The customer-facing product, brand, and relationship remain yours |
| Both | What data do we need to share? | Often none; where aggregated data helps, terms are set out first | Nothing moves until the boundary is agreed |
Living Lab connects the event to the hotel cluster, the inbound flights, and the origin markets. A mobility or logistics platform gets the demand shape days ahead, geotagged and timed.
The event calendar, the flight schedule, and the hotel booking curve are visible weeks before the demand hits your platform. Earlier signal lets you set price and position capacity before demand arrives.
Living Lab attributes the spike to a named event, route change, or market shift. Your product team can explain each spike with evidence.
Building it means assembling event data, flight schedules, hotel supply, and search signals across multiple vendors and geographies, then joining and maintaining them. Living Lab has already done that across 80 countries.
Demand vectors by place, date, origin, and expected duration. Living Lab names, sizes and times each peak before it arrives, so the operational response is planned.
A hotel pipeline decision is usually made on an occupancy trend, extrapolated forward. That answers a different question from the one a planner, a developer, or an investor faces: is the market short of rooms overall, or short of rooms on the 18 nights a year when demand peaks?
A new route photographs well on launch day. Whether it is still flying in year three, without deep discounting, and still worth the airport incentive that started it, is a quieter question, and it is the one that comes up at renewal.
International visitors to the United States fell 4.2% in 2025, the first annual decline since the pandemic, while worldwide travel grew. The spending did not disappear. It moved. Five economies are absorbing most of it, and the shift now looks structural.
During the 2026 World Cup, host-city hotel rates rose 13% on average. Miami rose 30.3%. New York fell 4.5%. The average was true and useless: it would have told all 16 cities the same thing, and been wrong for most of them.
We turn messy event and tourism data into decisions that help communities grow. Bring curiosity and rigour. We will hand you questions nobody has answered yet.
