Canada Restaurant Lead Finder - POS & Delivery Detection
Under maintenancePricing
from $5.00 / 1,000 base restaurant records
Enriched Google Maps leads for Canadian restaurants, all 13 provinces. Detects POS (Lightspeed, TouchBistro, Toast), payments (Moneris), delivery (SkipTheDishes, Uber Eats) and reservations. Bilingual EN/FR, Canadian postal codes. For restaurant SaaS and delivery sales teams.
Google Maps search queries. Combined with each location to form (term × location) tuples. Example: ['restaurants', 'cafe', 'pizza']. Quebec French terms work too ('restaurant', 'cafe', 'brasserie', 'poutine'). Up to 10 terms per run.
[ "restaurants"]Canadian locations to search in. Format: 'City, PROV' (2-letter province code), neighbourhood, or postal FSA. Province codes: AB BC MB NB NL NS NT NU ON PE QC SK YT. Examples: ['Toronto, ON', 'Montreal, QC', 'Vancouver, BC', 'Le Plateau-Mont-Royal', 'M5V']. Up to 25 locations per run.
[ "Toronto, ON"]Cap on records returned per (term × location) tuple. Hard max 100 to protect compute budget.
Determines which signals are extracted and which Pay-Per-Event charges apply. base = Maps profile only ($0.005/record). email = + scraped email ($0.010/record). premium = + POS/payment/delivery platform detection + reservation system + service area ($0.020/record). Set 'premium' to enable deep tech-stack detection (incurs additional compute time).
Restrict output to these restaurant types. Records that don't classify into one of these are dropped before enrichment. Leave empty to allow all.
[]Drop records matching a built-in chain blocklist (Tim Hortons, A&W, Swiss Chalet, St-Hubert, Boston Pizza, Pizza Pizza, La Belle Province, plus McDonald's, Starbucks, Subway and other cross-border chains — 130+ brands). Useful when targeting independents for SaaS sales.
Parallel HTTP fetches against restaurant websites. Lower if you hit rate limits. 4 is safe.
Name a persistent collection (e.g. 'toronto-db') to build ONE growing, de-duplicated lead database across many runs. When set, the actor skips restaurants already in this collection (deduped by Google place ID, BEFORE enrichment - so you don't pay to re-scrape them) and appends only NEW leads to a named dataset of the same name. Re-run with the same collection name across cities and search terms to keep growing it with no duplicates. Leave blank for a normal one-off run. Note: accumulate mode adds a per-run surcharge.