Use Apollo with an AI Browser for Data Extraction

If you use Apollo and you regularly need to extract structured data from websites, the bottleneck is usually the same: Apollo holds part of the context, but data extraction also needs signals that live outside it - on the public web, in LinkedIn, in news, in other connected apps. Strawberry is built to combine the Apollo context with the rest of the browser, and run the full workflow as a companion you can re-trigger every week.

This page describes specifically how Strawberry handles data extraction when Apollo is one of the inputs. It names the Apollo surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a researcher, ops manager, analyst, founder doing market analysis is trying to do

The goal of data extraction is to turn unstructured pages into a clean table or dataset. The success metric is concrete: extraction accuracy above 95% on spot-checked rows, dedup rate above 95%, completeness above 90%. That definition matters because it shapes what Apollo needs to contribute to the workflow.

What signals data extraction actually needs

For each signal below, here is whether Apollo can contribute directly or whether Strawberry has to find it via the browser:

• Source URL pattern (one page, paginated, search results) - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Target schema (which fields per row) - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Completion criteria (how many rows expected) - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Validation rules (which fields must be present) - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Login or paywall barriers - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Rate-limit posture of the target site - Apollo does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Apollo

Strawberry can search Apollo by ICP filter, enrich a candidate list with verified emails, and combine that with web research for personalized outreach.

Apollo surfaces Strawberry uses for this workflow: contacts, companies, sequences, lists, filters.

How Strawberry runs data extraction with Apollo

1. Strawberry opens the Apollo contacts that contains the relevant context.
2. The companion pulls related context from Apollo (companies, history, attached files) where it exists.
3. For the parts Apollo does not store, Strawberry uses the browser - web search, LinkedIn, news, the prospect's website.
4. Strawberry synthesises the output in the shape this workflow needs: A CSV or sheet with one row per extracted entity and a confidence column.
5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Apollo or your system of record.

Example Strawberry prompt

Paste this in a new Strawberry chat with Apollo connected. Adjust the specifics to your actual ICP, role, or topic.

Read this Apollo contacts and any linked context.
Then run a full data extraction workflow on it. Use the browser to fill any gaps not in Apollo.
Return the output in the shape we use for data extraction: A CSV or sheet with one row per extracted entity and a confidence column.
Do not send anything externally. Save the draft to me to review.

What a good data extraction output looks like

Here is what a finished output for data extraction should look like in practice. The specifics will change for your use case, but the shape should look similar:

• Source: company directory at example.com/companies, 30 pages of 50 companies each
• Target schema: name, website, employee count, HQ city, sector tag
• Expected rows: ~1500 (50 x 30)
• Validation: name + website required; sector tag from a fixed list
• Output: ./companies.csv with 1485 rows after dedup, 12 rows flagged for human review

Why Apollo for this, and where to use a different tool

Apollo is strong for this workflow because Strawberry can search Apollo by ICP filter, enrich a candidate list with verified emails, and combine that with web research for personalized outreach.

Where Apollo falls short Apollo credits are consumed per contact reveal; phone reveal credits are separate; account is shared so usage adds up fast.

Consider also a CRM (HubSpot, Salesforce, Pipedrive) once the prospect enters pipeline.

Common mistakes when running data extraction

• No schema defined upfront, leading to inconsistent rows
• Ignoring pagination and missing 80% of the data
• Extracting from logged-in pages without confirming the cookies are valid
• Hammering the target site without rate-limiting

Connecting Apollo to Strawberry

Apollo API key - reveal_phone_number is currently hardcoded true (known bug). Once connected, the companion can read the surfaces above without re-authenticating, and any write action still requires explicit human approval the first time the workflow runs.

Caveats

Do not let any AI agent send emails, update CRM records, or change shared systems without a clear approval step. Strawberry is strongest when the workflow combines browser context with connected-app context and a human review for sensitive actions.

When Apollo + Strawberry is the right combo for data extraction

Apollo is the contact and company database for outbound prospecting. Strawberry can search Apollo by ICP filter, enrich a candidate list with verified emails, and combine that with web research for personalized outreach For data extraction specifically, that means the agent already has 263M+ contact records with email and phone, company firmographics, technographics, intent signals as starting context - you do not need to brief it from scratch.

When it is NOT a fit

• You need a single number, not a synthesised brief. A SQL query against your warehouse is faster.
• The decision is happening in the next 60 seconds. The agent is fast but it is not instant; for hard real-time use, do it manually.
• The Apollo data you would feed in is stale or wrong. Garbage in, confident garbage out.

Three mistakes to avoid

1. no schema defined upfront, leading to inconsistent rows
2. ignoring pagination and missing 80% of the data
3. extracting from logged-in pages without confirming the cookies are valid

Honest tradeoff

Apollo credits are consumed per contact reveal; phone reveal credits are separate; account is shared so usage adds up fast If you are running this at scale (10+ briefs per day), batch the inputs and let Strawberry process them as a routine instead of one-by-one prompts - cheaper per brief and the output stays consistent.

What a real output looks like

Source: company directory at example.com/companies, 30 pages of 50 companies each
Target schema: name, website, employee count, HQ city, sector tag
Expected rows: ~1500 (50 x 30)
Validation: name + website required; sector tag from a fixed list
Output: ./companies.csv with 1485 rows after dedup, 12 rows flagged for human review