TopBookers: castings and agency bookers for models across Asia
Castings for models across Asia and the Gulf are scattered over Telegram channels, casting boards and job sites. TopBookers collects them every morning, reads the rate, dates, travel and visa terms, scores each offer and keeps a growing base of the bookers behind them.
- Offers in base
- 2,523 (273 open)
- Bookers
- 1,074
- Markets
- Asia and the Gulf
- Paid APIs
- None
What it does. Every morning it reads public Telegram channels, casting boards in 13 countries, job sites, Reddit and RSS, plus five search engines with queries in 41 languages. Each post is parsed for the day rate and total pay, dates and urgency, who is needed, city and country, whether flights, visa and housing are covered, requirements and contacts. Red flags such as unpaid or vague offers are marked.
Scoring and digest. Each offer gets a 0 to 100 score: pay 35, urgency 20, travel and contract terms 15, fit 30, minus red flags. A Telegram digest brings the top 25 as cards with the booker’s contact, a link to the post and a reply draft; anything already sent never repeats.
A base that grows by itself. Every contact in a casting becomes a booker with a track record: how many castings, the average rate, whether travel is paid. Every new channel or username it sees is checked: a channel with castings becomes a new source, a person becomes a booker. The base is saved every hour. Snapshot of 11 October 2026: 2,523 offers, 273 open, 1,074 bookers.
No paid APIs. Parsing runs on rules and dictionaries, without paid models or search APIs; reply drafts are refined on demand.
My part. I designed and built it: sources, parsing, scoring, the booker base, discovery and the Telegram bot.