A company that registered yesterday is one of the best outbound triggers there is. It has no accountant yet, no bank relationship locked in, no website, no payroll provider, no insurance — and nobody has pitched it eleven times already. The catch is timing: that window is open for days, not months. This is a practical recipe for pulling a fresh daily list of newly incorporated Canadian companies and turning it into an outreach-ready table in Clay (with Make/n8n alternatives).
Why "fresh" beats "big"
Most Canadian business lists you can buy are huge and stale — a million-row database that was assembled once and slowly rots. For prospecting a brand-new company, size is the wrong metric. You don't want every company in Canada; you want the ones that came into existence this week, before your competitors know they exist. Freshness is the whole edge. A 500-row list of companies registered in the last 7 days beats a 1,000,000-row list from last year for this use case, every time.
The data source
New federal Canadian companies are published by Corporations Canada. Instead of scraping it yourself, you can pull it through an Apify actor that outputs a clean, filterable dataset: Canada New Incorporations. The important input is a freshness filter — incorporatedWithinDays — so you only get companies registered in the last N days. Output is JSON/CSV, which is what makes the rest of this recipe trivial.
A note on honesty before you build a business on it: this is federal-level data, and the bulk source lags real time by roughly a week or two. That's fine for most outbound (you're not paging anyone at 3am), but don't promise your prospects or your boss "real-time." Know what you're working with.
The recipe: Apify to Clay
Clay is where a lot of outbound teams already assemble and enrich lead lists, so it's the shortest path from raw registrations to a running campaign.
- Add the source. In a Clay table: Add, Import data, choose the Apify integration, select the Canada New Incorporations actor.
- Set the freshness filter. In the actor input, set incorporatedWithinDays to 7 (or 1 if you want to run it daily and only see yesterday's additions).
- Map the columns. Clay will pull the actor's fields — company name, incorporation date, corporation number, jurisdiction, status. Map those to table columns. (Field names depend on the actor's current output — check the example dataset and map accordingly.)
- Enrich. This is where Clay earns its keep: from the company name, run Clay's enrichments to find the website, LinkedIn company page, and decision-maker email. Now a bare registration record becomes a contactable lead.
- Push to your sequence. Send the enriched rows to your email tool or CRM (Instantly, Smartlead, HubSpot, etc.) with a first line that references the trigger: "Congrats on incorporating [Company] — figured you might not have [your thing] sorted yet."
Schedule the table to refresh daily and you have a self-updating pipeline of net-new companies.
No Clay? Use Make or n8n
Same idea, different plumbing:
- Make or n8n: use the Apify "Run actor and get dataset" module, take the JSON output, drop it into a Google Sheet, Airtable, or straight into your CRM via its API. Add an enrichment step (Clearbit, Apollo, etc.) if you want contacts.
- Plain CSV: run the actor, export CSV, import to your tool of choice. Least automated, zero setup.
Who actually uses this
- Outbound, agencies, B2B SaaS — new companies as a daily trigger list.
- Accountants and bookkeepers — a company needs books from day one.
- Fintech, banks, business services (websites, logos, payroll, insurance) — reach the business before everyone else does.
- Market researchers — track company-formation velocity across the country.
The one thing that makes or breaks it
Speed of follow-up. The value of a new-incorporation lead decays fast — the same reason it's a great trigger is the reason it's a short window. Wire the pipeline so a new registration goes from "appeared in the data" to "first touch sent" in under a day, not next week. Automate the boring parts (pull and enrich) so the only thing you're spending time on is the message.
The dataset used here is the Canada New Incorporations actor — daily new federal registrations, filterable by freshness, JSON/CSV out. Questions about wiring it into Clay, Make, or n8n? Happy to help in the comments.
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