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How to use AI Score?

Written by Bourhan Sbalbal

THIS ARTICLE IS FOR: ✅ Self-Serve

Stage: Trial / Onboarding / Live

Owner: CS

Last updated: 2026-7-27

TL;DR

  • AI Score is ListKit's automated fit rating for how well a company matches your search criteria.

  • Options are Low, Medium, and High.

  • Use High to prioritize your strongest-fit leads first.

  • Best used after your other filters are already applied.

When you'd use this / Why it matters

Not every company that matches your filters is an equally strong fit. AI Score helps you rank and prioritize matches so you spend your credits and outreach effort on the companies most likely to convert.

Why the AI Score Filter matters

AI Score determines:

  • Which companies ListKit's AI considers the strongest matches to your criteria

  • How you prioritize outreach when working with large lists

  • Whether you're spending credits on marginal-fit companies

If you export a huge list without considering AI Score, you may be wasting outreach on companies that technically match your filters but aren't a strong fit overall.

Using AI Score keeps your lists:

  • Prioritized by likelihood of fit

  • Efficient with your credit spend

  • Focused on quality over pure volume

When you should use the AI Score Filter

Use AI Score when:

  • You've already applied Industry, Keywords, and other core filters

  • You have a large pool of matches and need to prioritize

  • You want to test outreach on your best-fit leads before scaling to a wider list

How the AI Score Filter works

  1. Apply your core targeting filters first (Industry, Keywords, Company Size, etc.)

  2. Open the AI Score filter

  3. Select Low, Medium, or High

  4. Click Save

Selecting High narrows your results to only the strongest matches based on ListKit's scoring model.

Example workflow

Step 1: Build your base list

Apply Industry + Keywords + Employee Range

Step 2: Check total matches

Review how many companies remain

Step 3: Apply AI Score = High

Narrow to your top-fit companies

Step 4: Export or expand

Export the High-score segment first, then run Medium/Low as a secondary batch if needed

Example use cases

Example 1: Testing a new offer

  • Filters: Industry + Keywords

  • AI Score: High

  • Outcome: A small, high-confidence test list to validate messaging before scaling

Example 2: Scaling an existing winning campaign

  • Filters: Same as a proven campaign

  • AI Score: High + Medium

  • Outcome: Expands volume while keeping most of the list high-fit

Example 3: Exhausting a niche market

  • Filters: Narrow Industry

  • AI Score: Low, Medium, High (all)

  • Outcome: Captures every possible match once the niche is small

Pro tips for best results

  • Always apply AI Score last, after your other filters are set

  • Start with High when testing something new

  • Don't rely on AI Score alone — it works best layered on top of Industry, Keywords, and other filters

  • If your High-score pool is too small, expand to Medium rather than loosening other filters first

Expected outcome

You should now be able to:

  • Use AI Score to prioritize your best-fit leads

  • Apply AI Score correctly alongside other filters

  • Make smarter decisions about credit spend

  • Scale outreach in a structured, fit-first order

Final takeaway

Think of AI Score as your quality control layer.

  • Your other filters define who qualifies

  • AI Score tells you who qualifies best

Use it to lead with your strongest opportunities every time.

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