Use Pipedrive with an AI Browser for Candidate Sourcing

If you use Pipedrive and you regularly need to source candidates, the bottleneck is usually the same: Pipedrive holds part of the context, but candidate sourcing 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 Pipedrive 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 candidate sourcing when Pipedrive is one of the inputs. It names the Pipedrive surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a recruiter, founder hiring, hiring manager is trying to do

The goal of candidate sourcing is to build a shortlist of 10-30 candidates who match the role and have at least one signal of openness. The success metric is concrete: 30% reply rate to first outreach, 5+ first-call conversions per 30 sourced. That definition matters because it shapes what Pipedrive needs to contribute to the workflow.

What signals candidate sourcing actually needs

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

• Current role and tenure - Pipedrive stores or surfaces this directly. Strawberry reads it through the connected integration.
• Recent role changes (often visible on LinkedIn) - Pipedrive stores or surfaces this directly. Strawberry reads it through the connected integration.
• GitHub or content output for technical roles - Pipedrive stores or surfaces this directly. Strawberry reads it through the connected integration.
• Company stage match (someone leaving a Series B is more likely to talk to a seed-stage co) - Pipedrive does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Geo match for hybrid roles - Pipedrive stores or surfaces this directly. Strawberry reads it through the connected integration.
• Openness signals (LinkedIn open-to-work, recent comments about job search) - Pipedrive stores or surfaces this directly. Strawberry reads it through the connected integration.

What Strawberry can do inside Pipedrive

Strawberry can scan stuck deals, enrich missing contact info, and prepare next-step recommendations per deal.

Pipedrive surfaces Strawberry uses for this workflow: deals, persons, organizations, activities, pipelines.

How Strawberry runs candidate sourcing with Pipedrive

1. Strawberry opens the Pipedrive deals that contains the relevant context.
2. The companion pulls related context from Pipedrive (persons, history, attached files) where it exists.
3. For the parts Pipedrive 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 shortlist with one row per candidate.
5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Pipedrive or your system of record.

Example Strawberry prompt

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

Read this Pipedrive deals and any linked context.
Then run a full candidate sourcing workflow on it. Use the browser to fill any gaps not in Pipedrive.
Return the output in the shape we use for candidate sourcing: A shortlist with one row per candidate: name, current role, target role fit (1-5), one personalised opening line, contact link.
Do not send anything externally. Save the draft to me to review.

What a good candidate sourcing output looks like

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

• Role: Founding Engineer (Stockholm or remote EU)
• Candidate: Marek Novak - Senior Engineer @ Klarna, 4 years
• Fit: 5/5 (worked on payment systems, contributed to Rust open source, recent talk on type-safe APIs)
• Opening line: noticed his RustConf talk on type-safe API contracts and our backend lead's tweet about Marek's library
• Contact: LinkedIn DM + GitHub email

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

Pipedrive is strong for this workflow because Strawberry can scan stuck deals, enrich missing contact info, and prepare next-step recommendations per deal.

Where Pipedrive falls short Pipedrive activity types are user-defined per workspace, so cross-tenant scripts need configuration.

Consider also Google Sheets for one-off lists.

Common mistakes when running candidate sourcing

• Spray-and-pray DMs that mention nothing specific
• Missing the obvious signals (someone just posted 'thinking about a change')
• No quality bar - putting 200 names on the list to look productive

Connecting Pipedrive to Strawberry

Pipedrive OAuth - Marketplace listing pending approval. 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.