You know your best customers. You want more accounts like them. The question is what “like them” actually means — and whether your tools can capture it.Most lookalike features match on firmographic attributes: same industry, same size range, same geography. That catches the obvious overlap but misses the real patterns. Your best customers might share a go-to-market motion, a technology architecture, a business model, or a market positioning that doesn’t map to any dropdown filter.Expand Lookalikes finds companies that are similar across what they do and how they describe themselves — not just what category they’re filed under.
You provide a set of seed companies — typically your best customers or closed-won accounts. The system finds what those companies have in common at a deeper level than firmographics, then surfaces thousands of similar companies you haven’t found yet, ranked by how closely they match.
Give it 10 seed companies, get back up to 20,000 scored lookalikes
Each result is ranked by a confidence score so you know which matches are strongest
Results combine with your other filters (size, geography, funding) so you’re not just getting lookalikes — you’re getting qualified lookalikes
Show What's happening under the hood
Seed companies are matched against a pre-computed similarity graph where every company has similarity scores to thousands of others, computed from description embeddings, keywords, and firmographic signals. If you provide a large number of seeds, clustering on the embedding vectors selects representative companies that span the diversity of your seed set. Scores are aggregated using Root Sum Square — a company moderately similar to 8 of your 10 seeds scores higher than one very similar to just 1 seed. This rewards broad ICP fit over single-account similarity.
Matching on industry + size + geography is a proxy for similarity. It catches companies in the same category but misses the ones that do the same thing in a different category. A developer tools company and a data infrastructure company might both be your ICP — same buyer, same budget, same pain point — but they’re in different industry classifications.Expand Lookalikes matches on what companies say about themselves (descriptions, keywords) and how they’re structured, not just which checkbox they fall under.
A company that’s moderately similar to 8 of your 10 seed accounts is almost certainly a better fit than one that’s very similar to just 1 seed but unlike the rest. The scoring formula captures this — it rewards companies that match the pattern of your ICP, not just one data point.
Show Why Root Sum Square?
RSS scoring computes sqrt(score_1² + score_2² + ... + score_n²) across all seeds. Squaring emphasizes stronger individual similarities while the summation rewards consistency across seeds. The square root normalizes the final score. A company similar to many seeds accumulates a high aggregate even if no individual score is extremely high.
