WorkerKitJoin the waitlistAI workers for Research
AI workers for research gather facts on a topic or company from the web and your own tools, then hand back a sourced brief. WorkerKit controls which internal apps they may read.
- Link Opportunity Finder Language model Finds the sites that link to your competitors but not to you, ranks them, and builds your prospect list.
- Competitor Newsletter Tracker Language model Reads competitor newsletters and archive pages, and only briefs Grok Bot when a High product, pricing, or GTM signal appears.
- Daily Hacker News Interest Brief Language model Find and summarize the Hacker News stories that matter most to your interests each day.
- Web Research Relevance Filter Decision model Finds and reads web pages on your research topic and returns relevant sources with typed judgments.
- Meeting Entity Relevance Finder Decision model Finds matching meeting that was actually about a customer, project, vendor, person or topic you name, including the internal ones the target was never invited to.
- Regulatory Pulse Language model Tracks SEC and industry rule drops for your beat, scrapes primary texts when needed, and hands Grok Bot a sourced compliance pulse.
- Competitor Site Pulse Language model Watches named competitor URLs for product, pricing, and messaging changes, then reports what actually moved with citations.
- Changelog Radar Language model Watches competitor changelogs and release notes and only surfaces High customer-impacting releases.
- Hiring Signal Monitor Language model Watches competitor and peer hiring signals — career pages, job posts, and "we're hiring" news — and briefs Grok Bot on what roles are heating up.
- Email Entity Relevance Finder Decision model Finds matching message that is actually about a customer, project, vendor, person or topic you name, internal and external, and names the false positives a keyword search returns silently.
- Launch and Feature Watch Language model Scans news and launches for your competitor watchlist and only briefs when a High launch or feature drop is evidenced.
- Morning Signal Diet Language model Builds a curated external morning briefing from your topic diet using news search and cited answers — not your inbox or calendar.
- Daily Competitor Blog Tracker Language model Monitor competitor blogs daily and alert you only to important changes from the past 24 hours.
- Funding and Deal Radar Language model Scans overnight funding, M&A, and strategic partnership news for your sector watchlist and ranks what Grok Bot should surface.
- Idea Grill Language model Interrogates a product or business idea with the hard questions before anyone builds it, checks the market, and writes a one-page verdict.
- Customer Complaint Miner Language model Mines Reddit and public review pages for the ten complaints people make most about a product or category, each with verbatim quotes and links.
- Influencer Discovery and Vetting Language model Finds new creators who fit your brand in Influencer Hero every week, vets each one on the web for brand safety and competitor deals, and posts a ranked shortlist to Slack.
- Competitor Newsletter Watch Language model Reads your competitors' newsletters, archives them after ingesting, watches their websites for changes, and reports what moved against your positioning.
- Project Brain Search Language model Answers the owner's questions from the project brain, citing the notes it used.
What is a Research AI worker?
A research AI worker is an AI agent set up to run the research job unattended, gathering facts on a topic, company, or question and returning a structured, sourced summary. It typically touches Google Search Console and web search, plus internal context in Notion, Confluence, Google Drive, and HubSpot or Salesforce records, reading only the sources and fields you have approved.
What these workers do
Brief a prospect before a call
Pulls public background on a company plus its HubSpot record, and returns a one-page brief so a rep walks into the call already informed.
Compile background from Confluence
Searches internal Confluence and Google Drive for prior work on a topic and assembles a sourced summary so nobody redoes existing research.
Track a query in Search Console
Reads Google Search Console for how a set of queries is performing and packages the findings into a short brief for the content team.
Answer a question with sources
Takes a specific question, gathers evidence from the web and approved internal docs, and returns an answer with each claim attributed to its source.
How a Research AI worker runs
What it connects to
When it runs
What stays under your control
Research questions, answered.
Can a research AI worker read my internal documents?
Only the internal sources you approve, alongside public web data. A research worker can pull from Confluence, Notion, Google Drive, or CRM records you grant it, and WorkerKit scopes it to exactly those. It reads them to build a sourced brief, and anything outside the grant, or any redacted field, stays hidden.
How is a research worker different from enrichment?
Research produces a standalone brief on a topic or company, enrichment fills specific fields on an existing record. A research worker gathers and writes up findings for a human to read. An enrichment worker instead looks up missing attributes and adds them to a CRM contact. The jobs differ in output and stay separate.
Does a research worker cite where facts came from?
Yes, a research worker attributes each claim to its source so you can verify. Whether a fact came from the web, a Confluence page, or a HubSpot record, the brief points to where it was found. WorkerKit also logs every source the worker read, giving you a second record of what informed the output.
Will a research worker change any records while gathering?
No, research is read-only on its sources and writes only the finished brief. A research worker never edits the CRM records or Drive files it reads for context. WorkerKit keeps it read-only on every source, so gathering facts leaves your systems untouched while the summary lands where you send it.