How I’d Use AI Auto-Match

AI Auto-Match is powerful.
But like any tool, how you use it determines how much value you get.

The first step is to define the eligible pool.
Instead of matching from your entire database, create a filtered list of candidates based on prior outcomes or performance signals.

This lets you rank and match with precision.

Think of this like a waterfall.
Each step widens the net but only after the previous pools have had a chance to deliver.

Here’s how I’d structure it:

Start with candidates who have been placed before and had positive outcomes.
You’ve seen them succeed.
They’re already vetted.

This is your highest-confidence pool.

If Automation 1 doesn’t generate enough quality submissions, we go to Automation 2.

This pulls from candidates who have previously been submitted and received positive client feedback.

Still a strong signal. Still worth prioritizing.

If you still need more candidates, widen the pool again.

This time, target candidates who score 85%+ in AI match relevance but may not have a submission or placement history yet.

Good fit. Less context. Still valuable.

The key is what controls when the next automation kicks in.

Each automation runs only after the job has completed the previous automation AND only if the number of total submissions is below your threshold.

For example:

• If Automation 1 yields 20 great matches, maybe that’s enough

• If not, then and only then, Automation 2 triggers

• And if you still need more, then Automation 3 runs

You can set limits like:

• Stop once 25 total matches are found

• Or keep going until 50 or 75

• Or pause all matching after 72 hours

Here are some ways to go even further:

Add an automation layer that prioritizes candidates who:

• Have updated their profile recently

• Engaged with an email

• Clicked on a job

• Responded to outreach

• Or maybe you added that field every company should have …. the Date of Last Meaningful engagement 😉

You can even build in candidate messaging like:

“View or click on a job within 7 days to be prioritized for new roles”

This drives marketing engagement while reinforcing a positive feedback loop for candidates.

Avoid matching candidates who aren’t available for another 60 days.
Filter by “Date Available” dates to keep matches timely and relevant.

This can also reduce recruiter churn on unresponsive or unavailable candidates.

By tracking performance at each automation layer, you’ll start to understand:

• Which pools convert to placements faster

• Where your top matches really come from

• How many total candidates you need per role by job type or client

• Where your best placements really come from

That data feeds your optimization loop and helps you tune each layer over time.
And in time get even better matches!

AI Auto-Match isn’t just about speed.
It’s about control and quality.