How Recruiters Use AI Browsers for Prospect Research

This guide is for recruiters who run prospect research. It explains how an AI browser like Strawberry runs the workflow given the tools a recruiter actually uses every day, what the output should look like, and where the workflow fits in the recruiter's week.

Why this matters for recruiters

A recruiter spends time on this: source, screen, and close hires across multiple roles, often without a dedicated sourcer or coordinator. The pain that makes prospect research feel slow is real: sourcing eats the day; screen calls compete with intake; coordination of interviews falls on the recruiter. The reason an AI browser helps is that recruiters already use multiple surfaces (LinkedIn (Recruiter when licensed), Greenhouse or Ashby or Teamtailor, Gmail, Google Sheets, Notion) to do this work, and the browser is the only tool that can read across all of them and produce a finished output.

What success looks like

The goal of prospect research is to decide whether a prospect is worth a calendar slot and prepare a personalised first touch. For a recruiter, success metric is concrete: first reply rate above 8% and a meeting booked in under 14 days from first touch. A finished prospect research run should look like this: a shortlist of 10-30 candidates with role-specific personalised openers, fit notes, and contact links.

Signals prospect research needs

The workflow needs these signals: role tenure and seniority on LinkedIn; recent funding rounds or M&A activity; headcount growth or layoffs in the last 6 months; tech stack and procurement signals. For a recruiter the practical question is which signals come from the tools already in the stack (LinkedIn (Recruiter when licensed), Greenhouse or Ashby or Teamtailor, Gmail, Google Sheets, Notion) versus what the browser has to fetch. Strawberry reads the in-stack tools through native integrations and uses the browser for the rest (LinkedIn, news, company websites, search). The recruiter stays in one surface.

Paste-ready Strawberry prompt

I'm a recruiter. Run prospect research for me using LinkedIn (Recruiter when licensed), Greenhouse or Ashby or Teamtailor, Gmail and the browser, then save the draft.

What a finished prospect research output looks like

Concrete example, not a placeholder:

• Anna Lindqvist - VP Marketing, Voi Technology
• ICP fit: yes (Series D scooter co, EU expansion, 1500 employees)
• Talking point 1: hired 4 paid-acquisition managers in last 90 days - clear shift toward performance marketing
• Talking point 2: spoke at SuperVenture last month on scooter unit economics
• Talking point 3: company just announced Germany pull-out - retention focus is likely a priority
• Suggested first message: short, references the SuperVenture talk, asks one specific question, no calendar link

When this works, and when it does not

This workflow is right for recruiters when the work is repeatable and crosses multiple tools. It is wrong when spray-and-pray DMs that mention nothing specific about the candidate. In that case, the recruiter should keep doing the work manually until the pattern is clear enough to automate.

Three mistakes to avoid

• Researching prospects who don't match ICP - the brief is wasted
• Generic talking points ("impressive growth") that don't reference any real signal
• Copying public bio text instead of synthesising fit

Caveats

Strawberry holds back on sending email, updating CRM records, or changing shared systems until a human approves the action. Treat the agent as a fast first-draft author, not an autopilot.