Most teams burn outreach budget on leads that were never going to convert. Quicklead's AI reads each LinkedIn profile, compares it to your ICP description, and gives you a score with reasoning — before the first message goes out.
No rigid forms, no checkbox fields. Type a single paragraph describing your ideal customer (title, geography, company size, tenure, industry, signals). The AI parses it into scoring criteria.
Quicklead reads each contact's profile (title, company, headcount, location, tenure, recent activity) and scores them against your description. Color-coded "Good fit" / "Maybe" / "Weak fit" buckets.
Each score comes with a one-line explanation of why — e.g., "Matches US location, SaaS size, VP Sales title; 4 yrs in role (too long)". No black box.
Set a score threshold (e.g., 70+) and exclude weak fits from outreach. Stop burning credits, connection-request slots, and Open Profile windows on people who would never convert.
Discover relevant LinkedIn posts by topic ("cold email"), hashtag (#leadgeneration), or industry term. Quicklead surfaces the posts that match.
Pull the people creating, engaging with, or sharing those posts as leads. Engagement = intent. These are warmer than cold lists by default.
Every extracted contact gets scored against your ICP description the moment it's pulled in. No manual filtering, no second-pass cleanup.
One click moves "Good Fit" extracted leads (e.g., 80+ score) into a Quicklead campaign with Open Profile InMail + connection-request routing baked in.
The moment a list lands in Quicklead (Sales Nav URL, CSV, People Search, social signal extract), AI scoring runs in the background. By the time you build the campaign, scores are ready.
Visual color-coded buckets so you can target tier-1 first. Most teams skip the bottom 35% entirely and only send to scores of 70+.
When you only message good-fit leads, reply rate climbs 30–50% and you stop burning paid InMail credits on prospects with no chance of converting.
Update your ICP description (e.g., "now focus on Series B+ instead of seed"). Quicklead re-scores existing lists against the new definition in seconds.
Sending 1,000 messages to 1,000 random leads is noise. Sending 400 messages to 400 ICP-matched leads is pipeline.
Forget rigid rule-based filters. The AI reads context: title nuance, recent posts, tenure, headcount, geography. The same nuance a human SDR uses to qualify a list — at 1,000× the speed.
When you only outreach Good Fit leads, reply rates climb by a third to a half. Same campaign, same message, half the noise.
Standard $10/credit overage adds up fast when you’re sending to 30% of your list that was never going to convert. ICP scoring strips those out before send.
No engineer required to set up scoring rules. Write your ICP in one paragraph, get scores with one-line reasoning — the entire team can audit and refine.
Book a 20-minute demo. Bring one of your existing lead lists, describe your ICP in a sentence, and we’ll score it live — you’ll see which 30–40% to drop.
Find answers to common questions about AI ICP Scoring.