AI can enrich contact data automatically when every field remains tied to a source and confidence level.

Decide which fields are worth enriching.

More fields do not always produce better outreach or better reporting.

Start with the data your team will actually use: company website, current role, seniority, location, account match, or a link to a public profile.

For each field, define an acceptable source and what counts as unknown. This prevents an agent from filling empty cells with weak inference just because the database prefers completeness.

Match identities before you trust the new data.

A name, employer, city, and public profile can help establish that a source refers to the correct person. One of those signals alone may be misleading, especially for common names or people who recently changed jobs.

Strawberry can gather the evidence from the browser pages you choose and present unclear matches separately. That lets a researcher decide whether a record should be updated, held, or excluded.

Keep provenance with the data after the research pass.

A title without a source becomes hard to trust six months later.

Store a source link, collection date, and confidence note in the enrichment output or adjacent research record, especially for fields used in segmentation or sales messaging.

When two sources disagree, preserve the conflict rather than selecting the more convenient answer. A public bio may lag behind a company team page, and neither may be current enough for a high-stakes decision.

Enrich in batches with a review gate.

Start with a small cohort, review the matches and sources, then adjust the rules before processing a larger list. Once the evidence standard is stable, save it as a skill and run a routine that flags stale records or fills clearly sourced gaps.

For record updates, keep a separate proposal queue. Your CRM remains the system of record, while the enrichment report shows why a field should change.

Frequently asked questions