How to add Clay prospects to Attio

You send Clay prospects to Attio with Clay's native Attio integration. An Attio workspace admin connects Attio from inside Clay by signing in with Attio, and from then on you can add Attio actions to any Clay table. Each action works like an enrichment column: it runs on every row unless you give it a condition, and its result shows up in the cell. The actions cover the jobs you'd expect:

• Create Record adds a new record to an Attio object, such as People, Companies, or Deals.
• Upsert Record creates the record or updates the one that already has the same email address or domain.
• Update Record changes fields on a record you already have.
• Lookup Records finds existing records so a later column can decide what to do.

You map each Clay column to an Attio attribute, which is what Attio calls a field, and you can turn on auto-update so new rows flow into Attio as your table grows. If you'd rather not connect the two, export the Clay table as a CSV file and import it into Attio's People object, mapping the columns there instead.

Both routes move data well, but neither decides which rows deserve to become records. A Clay table usually holds everyone a search returned, and its enrichment columns fill in details without judging fit. The person who changed jobs since you built the table still goes in. So does the contact at a company that already pays you, and the row whose email came back risky. To keep those out, you add a condition column. To avoid creating a second company for an account you already track, you upsert by domain or run a lookup first. Every one of those steps is possible in Clay, and every one is a column you design, test, and maintain as your table changes. Getting people onto the right list at the right stage isn't one of the integration's actions, so that takes a separate step outside it.

A good result in Attio looks like this. Each prospect is one person record linked to one company record, with no duplicates. Each person is an entry on the list your team works from, at the stage where new prospects start. And the reason they were added is on the record, so whoever opens it knows why to reach out without redoing the research.

Kite does that work for you. It qualifies the rows from your Clay table, checks Attio for the people and companies you already have, and adds the prospects to your list with the research attached.

Start by exporting your Clay table as a CSV file and sharing it with Kite, along with where the prospects belong:

"Here's our Clay table of heads of RevOps. Add the ones who fit to our Outbound list in Attio."

Kite is an AI marketer. It researches your business, builds a growth strategy, and ships the work that brings you new customers. By the time you share a table, it already knows your ideal customer profile, your current customers, and your competitors from that research, so it can judge your Clay rows against a clear standard without a long brief. If your table carries a judgment of its own, such as an AI research column or a score you built in Clay, tell Kite how much weight to give it.

You'll need to connect your Attio workspace so Kite can read your objects and lists and write records to them. Kite doesn't connect to your Clay workspace, so the CSV export is how your table reaches it. If your team has added Kite to Slack, share the file in a Slack thread with Kite. If you work with Kite in the Kite web app, import the export into a Google Sheet, connect your Google account, and tell Kite which sheet to read.

Adding prospects to your CRM is routine work, so Kite writes the records and then tells you what it did. If you'd like to see the list before anything reaches Attio, say so in your request.

Kite reads every row and column in the export and works out what each column holds. A typical Clay table carries a full name, job title, company name, company domain, LinkedIn profile, and work email. It often has enrichment columns too, such as headcount, industry, funding, the technology a company uses, or the output of an AI research column. Kite keeps all of it, because a detail Clay already found is one it doesn't need to look up again.

Kite also notes what the table can't tell it. A Clay row records what was true when the enrichment ran, so a table built a few months ago describes some roles people have since left. A column that came back empty, such as an email none of your Clay providers could find, becomes a gap for Kite to fill rather than a reason to drop the row.

Kite decides who belongs in Attio by checking each row against your profile before it writes anything. First it confirms that the person still holds the role at that company, using a current public profile or the company's own pages, and it drops anyone who has moved on. Next it removes your existing customers, your competitors, and anyone who has opted out of hearing from you. Kite also searches your Attio workspace for each company, which shows whether an account is already a customer or sitting in an open deal.

Every prospect who passes gets a fit score from one to five, with a one-line reason, such as "VP of revenue operations at a 300-person logistics software company that just opened three sales roles." When the list runs past about 15 people, Kite splits it into primary and secondary groups so your team knows where to start.

If fewer rows qualify than your table holds, Kite tells you how many it kept and why the rest fell short. A short list of good prospects is worth more in Attio than a long one your team has to clean up.

Kite fills in what the table is missing through its own research sources, so you don't need another data provider account for this part. For a person, that's the work email, current title, seniority, and LinkedIn profile. For a company, it's the domain, industry, employee count, and location. To find a missing work email, Kite tries several email providers in turn and stops at the first one that finds a work address for the person. Then it verifies that address.

Each email keeps its verification result exactly as it came back, whether that's verified, risky, catch-all, or unverified, so your team can tell which addresses are safe to send to. When no work email turns up, Kite marks it as not found rather than guessing one from the company's address pattern. Kite includes only business details, which means it leaves out personal email addresses and phone numbers.

Kite reads how your Attio workspace is set up before it writes, because every team shapes Attio differently. It lists the attributes on your People and Companies objects along with their types: which hold text, which hold numbers, which are select fields with a fixed set of options, and which link to another record. It also reads the list you named, including its own attributes and the stages in its status attribute.

That lets Kite put each piece of research where your team expects it. The job title goes into the person's job title attribute, the LinkedIn profile into the LinkedIn attribute, and the fit score into a number attribute rather than a block of text. When an attribute is a select field, such as Lead source, Kite uses one of the options you already have. If your workspace has nowhere to keep something useful, such as the fit score or the email's verification result, Kite proposes a new attribute and creates it once you agree.

Kite matches every prospect against your existing records, so the same person or company never appears twice. Attio treats a person's email address and a company's domain as unique, and Kite uses them to decide whether to update a record or create a new one. For a prospect without a work email, it searches your People records by name and company before it creates anything.

When Kite finds an existing person, it updates only the attributes it has new information for, such as a changed job title, and leaves the rest of your team's edits alone. When the company already exists, Kite links the new person to it instead of creating a second company.

Kite creates or updates the company records first and then the people, so every person arrives linked to their company. A company record gets its name, domain, description, and location. A person record gets the following:

• First and last name
• Work email
• Job title
• Company, as a link to the company record
• LinkedIn profile
• Fit score and email verification result, in the attributes you agreed on

Then Kite adds each person to the list you named, sets their stage, and fills in any list attributes your team uses, such as an owner or a priority. If someone is already on that list, Kite updates their entry instead of adding them a second time.

Finally, Kite attaches a note to each person record. The note explains why the person fits, what Kite checked to confirm their role, and any recent event at their company worth mentioning, such as a funding round or an open sales role, with a link to the source. That's the research a rep would otherwise redo before writing the first email.

When the work is done, Kite posts a summary in the conversation where you shared the table. It says how many people and companies it created and updated in Attio, how many it added to the list, and which rows it skipped and why. It also reports how many rows from your table made the cut and how many of those have a verified work email. The full list comes with it, so you can scan the results without opening Attio.

From there, Kite can keep the work moving. It can create an Attio task for the teammate who owns each primary prospect, with the record linked and a due date. It can draft a personalized first email for each primary prospect from the research on their company. It can also hand back a CSV file of the qualified rows with their scores and reasons, so you can bring Kite's verdicts into your Clay table. When you have a new Clay export, share it in the same conversation, and Kite qualifies that batch against everything already in Attio before adding anyone.

When Kite finishes, your Attio workspace holds a clean set of qualified prospects on the list your team works from, and your conversation with Kite holds the record of what changed.