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Updated October 8, 2026
How can I find lookalike companies?
Find lookalike companies by describing your best customers in traits a company database can search (industry, size, funding stage, location) and searching for other companies that share them. A lookalike is a company that matches a reference group on those traits. Tools differ mainly in how they choose the traits: a model, AI clustering, or the filters your seeds share.
- Start from customers you want more of: the accounts that bought quickly and stayed.
- Name the traits: industry, headcount band, funding stage and headquarters location are fields every company database holds.
- Search, then subtract: remove the companies you already sell to or already work in the CRM.
- Add the people: find the buyers at each company and their work emails.
In Cleanlist AI the Lookalikes of best customers agent does all four on a schedule. You paste the seed domains once, and each run adds companies it has not delivered before, with the buyers at each.
How to find companies similar to other companies?
By hand it takes four steps: write down what your best customers have in common, search a company database with those traits, remove the companies you already know, and find the buyers at the ones left. It works well for one list. The work comes back every month, because new companies keep appearing and last month's list needs subtracting too.
- List the shared traits: open each best customer's profile and note its industry, headcount band, last funding round and headquarters country.
- Search: run a company search with those values. Treat each trait as a list: any of the industries, any of the size bands, any of the funding stages.
- Subtract: remove your customers, open opportunities and every account already in the CRM. Match on website domain; company names vary too much.
- Find the buyers: search each company for the titles you sell to, then find and verify their work emails.
Next month, run it again and remove last month's companies as well. That repeat, with the dedupe against the CRM and against past runs, is the part the agent takes over.
Pick the seed list: the ten customers you most want more of
Pick customers you would sign again tomorrow: they bought quickly, use the product and renew. Ten is a good start, and the agent takes up to 25. Leave out a large logo that looks nothing like the rest: every seed's industry, size band and funding stage joins the search, so one outlier widens it for all of them.
- Same buying motion: seeds sold by the same team, with the same pitch.
- Typical size: the headcount band where most of your good customers sit.
- Recent wins: customers from the last year reflect today's product and pricing.
- Domains only: paste website domains; each has to match one company exactly.
If your best customers fall into two clear groups, such as agencies and software companies, give the agent a second play with the second group's seeds. An agent holds up to 10 plays and still counts as one of Pro's 5 active agents.
What makes two companies similar, and what Cleanlist AI matches on
Two companies are similar when they share the traits that predict a sale for you. The agent matches on three, taken from your seeds: industry, headcount band and last funding round type. A match needs one of the seeds' industries, one of their size bands and one of their funding stages, plus any filter you add.
| Trait | What the agent does with it |
|---|---|
| Industry | Matched: any of the seeds' industries |
| Company size | Matched: any of the seeds' headcount bands |
| Funding stage | Matched: any of the seeds' last funding round types |
| Headquarters location | Add it as a filter beside the seeds |
| Other company filters | Add any company search filter beside the seeds |
| Tech stack | Not used: the agent does not match on tech stack |
| Buying intent | Not used: Cleanlist AI has no intent data |
| Product or website similarity | Not used: there is no similarity model |
The agent reads the seeds' traits once and refreshes them every 30 days by default (Refresh seed facets every). When a seed raises a new round or grows into a new size band, the search follows on the next refresh.
Firmographic traits are what every company database records. For what they cover and where they come from, see what firmographic data is.
The play: a weekly company search, then the buyers at each
Each run, the company search returns companies with the seeds' traits, removes the seeds and the companies already in your CRM as accounts, and keeps only companies this play has not delivered before. Find People takes up to the number of buyers you chose at each, Enrich finds their work emails and Notify posts the list.
- CRM accounts left out: matched by website domain, the Account's Website in Salesforce or the company domain in HubSpot. An account with no website cannot be matched, so it can come back.
- Each company once: a company this play delivered stays delivered; the next run brings only new ones.
- The weekly number counts people: with Find People in the play, People per week counts buyers, so 80 a week at 2 a company is about 40 companies.
- Sizing comes first: Clu's audience count is taken before CRM accounts are removed.
If the CRM's domain field cannot be read, the run stops with an error instead of suggesting companies you may already have. A seed that cannot be matched to one company stops the run the same way, so fix the domain and run again.
Lookalike companies vs lookalike audiences in ad platforms
A lookalike company list is a set of named accounts your team can research and contact. A lookalike audience is an ad platform's model of people who resemble a seed list: you target it with ads and the platform picks who sees them. The first feeds outbound and account research; the second feeds paid campaigns.
- Google Ads builds Lookalike segments from your seed lists for Demand Gen campaigns.
- LinkedIn discontinued its lookalike audiences on February 29, 2024 and points advertisers to Predictive Audiences and Audience Expansion.
- Lookalike modeling is the general method behind all of these: describe a seed group by its traits, then find more of a population that shares them.
The two work together. LinkedIn builds predictive audiences from company lists, so once the agent's list is large enough, the same companies can seed an ad audience while their buyers get outbound.
What are some websites I can use to find similar companies?
Crunchbase, Clay, Apollo, Ocean.io, ZoomInfo and PredictLeads all find similar companies, and they match in different ways: a machine learning model, AI clusters over your seed list, or the traits your seeds share. Pick by what comes next: research on one company, a workflow you maintain, or buyers with contact details.
| Tool | How it finds similar companies | Best when |
|---|---|---|
| Crunchbase | A Similar Companies tab on each profile, from a machine learning model that looks at attributes such as industry and growth signals; shows which matches are already in Salesforce | You research one company at a time |
| Clay | Lookalikes from an audience or table of up to 15,000 companies, grouped into AI-generated clusters by industry, size and use case | Your team already builds lists in Clay |
| Apollo | A Sales Play that finds lookalike companies each week from 3 to 5 of your best customers and adds them to a list | You prospect and send from Apollo |
| Ocean.io | An API that searches companies by filters or lookalike domains, plus a separate lookalike people search | You want lookalikes inside your own tools |
| ZoomInfo | A Find Similar Companies tool in its MCP server | You have ZoomInfo and work in an AI assistant |
| PredictLeads | Company lookalike data on 18.5M+ companies, delivered as a dataset | You build your own models from raw data |
| Cleanlist AI | Facet matching on up to 25 seed domains (industry, size band, funding stage), CRM accounts left out, buyers and work emails added every week | You want new companies and their buyers on a schedule |
Each vendor's description comes from its own pages, read October 8, 2026 (see Sources). Where Cleanlist AI fits: it is the scheduled option, with the CRM subtraction and the buyers built in. For a one-off look at a single company's neighbors, a profile tab or the MCP tool below is quicker.
Find similar companies inside Claude with the MCP server
With the Cleanlist MCP server connected, Claude gets a find_similar_companies tool. Give it one company's domain and it returns up to 100 companies in the same industries and size band, each with a score from 0 to 1 for how many of the reference company's traits it shares. It answers in the chat, one company at a time.
Use it to explore a market or check one account's neighbors, then ask Claude to search the buyers at a few of them with the same server's people search. Use the agent for the standing job: many seeds, CRM accounts left out, only new companies, every week.
The MCP server is included from Starter and on the 14-day trial; it is not on the Free plan. The scheduled agent runs on Pro. See how to connect the MCP server.
What it costs, and which plan runs it
The company search and Find People are free. The agent pays 1 credit for each work email it finds, 10 for each phone number if you turn phones on, and nothing for a miss. At 80 buyers a week with emails only, that is at most 80 credits a week, or about 347 a month.
Running the agent needs Pro (from $89 a seat a month, $67 billed annually), which includes 5 active agents across the workspace, or Enterprise for unlimited agents. The 14-day Pro trial lets you build the agent and review its play; testing it and turning it on need a paid plan.
Every agent Clu builds carries two limits you set: records per run, and a credit limit counted across all its runs in each schedule period, a week or a month here. When the agent reaches it, runs stop until the next period. Set records per run to at least the weekly number of buyers, or the agent delivers fewer.
