The short answer

  • What launched. Beacon Pro, a self-serve account for anyone who needs a year of advertised rates for a list of properties. Its first tool is Rate Capture.
  • What it does. Give it a list of properties. It returns every rate they are publicly showing, for every check-in date across the next 365 days, as a CSV.
  • What it costs. 1 credit per property per run. A credit is £1. Six free credits with a new account, no card and no subscription.
  • Who it’s for. Consultants, management companies, developers, investors, owners, lenders, vendors, researchers and journalists. Anyone who needs the rates but doesn’t set them.

Beacon Pro is live, and its first tool is Rate Capture. Paste a list of property URLs, run a report, and download every rate those properties are showing for the next 365 days. One credit per property, a credit is £1, and the first six are free.

If you need to know what a market is charging next March, and you don’t set the prices in it, that information has been out of reach. Rate shopping is a subscription built around one hotel and the handful of competitors it nominates, running every day for that hotel. Benchmarking reports look backwards, depend on who takes part, and return a blended monthly figure with no room type or rate plan.

Rate Capture closes that gap: any list of properties, every rate, a year ahead, in a few minutes.

Running a hotel? Rate Radar is built for you. It checks your own market every day and whenever you ask, and flags the moves worth acting on with Signals. What follows is for everyone on the other side of the table.

Who it’s for

The mechanism is the same every time. The reason changes completely.

Consultants and management companies

Walk in with the numbers

A prospect’s comp set, sales and undercuts, with no login needed.
Prompts for this work →

Developers and investors

Test your rate assumption

What the catchment asks by room type, weekday and season, a year out.
Prompts for this work →

Lenders, valuers and surveyors

Evidence for the file

A dated, fixed record of what an asset and its peers are asking.
Prompts for this work →

Owners and asset managers

Check your operator

The asset’s pricing next to a comp set you choose, run again each month.
Prompts for this work →

Revenue and technology vendors

Feed your own model

Every rate as its own row across up to 24 columns, ready to parse.
See the API →

Destinations, researchers and press

Measure an event

Event nights and the weeks around them, same properties, one file.
Prompts for this work →

What comes back

A report includes one row per rate, per room, per check-in date, for every property on your list, across 365 days from the day it runs. Nothing is averaged, sampled or rounded off.

Eight columns are always there: the property, which site the rate came from, the check-in date, whether it was available, sold out or only sold with a longer minimum stay, the room, how many guests the rate is for, the price and its currency. Then you tick on whatever else the job needs:

  • Rate plan terms. The meal plan (room only, breakfast, half board, full board, all-inclusive and more), free cancellation, non-flexible.
  • Sale badges. Whether the rate was discounted on the day, so a promotion is never mistaken for a standard price. Sale badges come from the world’s largest online booking site, so they’re blank for properties captured from a chain’s own website.
  • Rooms left to sell. How many of each room type are left when stock runs low, and the full count where a site holds it.
  • Cheapest-rate markers. The cheapest rate of the day, and the cheapest the property itself is selling.
  • Parity issues. When a partner offer or targeted deal is available, if it’s cheaper, and by how much. This covers properties on the world’s largest online booking site.

Sold-out dates stay in as rows with no price, and so do dates a property only sells as part of a longer stay, labelled with the minimum it needs. A date with no rate isn’t a hole in the file. It’s a date the property wasn’t selling to that search, sold out or held for longer stays, and the file says which. A market with fifteen sold-out Saturdays tells you something no average will.

A twenty-property report can run to hundreds of thousands of rows, and every file is stamped with the date and time it was captured.

Price ladder for one property on one check-in date at a two-night stay: 24 rates across four room types. Standard Double from £310.38, the cheapest of the day, with four rooms left. Executive Suite and Signature Suite between £545 and £721. Grand Deluxe Suite up to £1,602.90, with one room left.
One property, one check-in date, 24 separate rates. A tool that hands you a single number for that day has thrown 23 of them away. This report was captured in GBP: you choose the currency for each one.

How it works

  1. Build a report. Paste the URL of each property’s page on the world’s largest online booking site, or on the website of a major hotel chain that sells mainly direct, then pick one currency and a stay length from one to seven nights.
  2. Run it. One credit per property, with the cost shown before you commit. It takes a few minutes.
  3. Export it. Choose your columns, check the preview, download the CSV, or read it through the API.
  4. Run it again. Each run is dated and fixed once it completes. Clone the report later and run the copy, and both snapshots sit side by side in your account.

Pricing: £1 a credit, one credit per property per run, six credits free when you sign up. A ten-property study is £10, a hundred-property city study is £100, and a seven-property comp set checked monthly is £7 a month.

You can use the file in client work and as an input to your own models, research and products. The terms set out the detail.

Nobody reads 200,000 rows

Some will pivot the file in Excel. Others will hand it to an AI tool, so every finished report comes with over 160 ready-made prompts, written for your line of work. Each one starts with setup text that explains the file to your AI tool. Copy a prompt, attach the CSV and ask. AI tools can get figures wrong, so check key numbers against the CSV.