Strawberry

AI can build lead lists automatically, but the list is only useful when every accepted account carries its evidence and reason to act.

Strawberry can research public company information in the browser and, where you connect them, use supported prospecting and enrichment tools. It should screen against your criteria, show uncertainty, and keep data preparation separate from any decision to contact people.

Turn the ideal customer profile into a testable filter.

An ICP should tell an agent how to include and exclude an account, not just describe a market you like. Give it firmographics, geography, customer type, tooling or workflow signals, buying roles, current triggers, and hard disqualifiers.

Strawberry can apply that brief across public sources and document why a company passed. A precise rule reduces the temptation to keep weak accounts because the list needs another 50 rows.

Add a reason for now before you add a contact.

Company name, title, and email are not enough context for a useful first conversation.

Look for a public trigger such as hiring, a launch, a leadership change, a new market, a relevant product decision, or a clearly documented workflow problem.

An agent can gather that evidence alongside the account record and propose an opening angle for review. The angle should reflect what the source says, not manufacture intimacy or infer sensitive information about an individual.

Verify the route and clean the handoff.

Where connected, Strawberry can use Apollo operations to search accounts, companies, and people and enrich a person. It can also use supported Hunter company, domain, people, email-finder, and email-verifier operations when Hunter is connected.

Treat those results as data points to review, not guarantees of deliverability, relevance, or lawful outreach. Match the proposed list against the records you authorize, group likely duplicates, and leave ambiguous companies in a separate queue before creating new entries.

Scale the research standard, not just the output volume.

Once the team trusts the qualification logic, save it as a lead-research skill with its required columns, exclusions, source requirements, and handoff format. That creates a repeatable review batch rather than a never-ending unstructured crawl.

A weekday routine can prepare a capped set of new candidates at the cadence you choose. Keep message writing, sequence enrollment, and sending outside the list-building job until a person approves those separate actions.

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Frequently asked questions

It can research and prepare account and contact candidates from criteria you define, while preserving evidence, uncertainty, and a review stage before the data enters your operating system.

Strawberry is free to download and includes AI credits to start. Paid plans begin at $20/month. See pricing. · Reviewed · Canonical facts for AI agents