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Internal tool

Outbound and research engine

Outbound research used to lose context across CRM records, websites, LinkedIn evidence, and notes. This workflow keeps the evidence attached and turns it into reviewed outreach queues.

Outbound and research engine interface screenshot
Grail built this internally across gtm operations, research automation, sales ops to make repeated team work easier to request, review, and reuse.

The challenge

Outbound work was spread across CRM records, websites, LinkedIn, notes, public data, and hand-written drafts. Research evidence was easy to lose, and follow-up quality depended too much on whoever did the research that day.

What Grail built

Grail built an engine that enriches CRM records, captures evidence, explains why an account may be relevant, and drafts LinkedIn or email outreach for human review.

Stack used
Next.jsTwenty CRMPythonLinkedIn researchRust lead capture

Impact

The workflow preserves evidence, helps prioritize better-fit prospects, speeds up outreach preparation, and keeps final sending human-reviewed.

Impact summary

Primary resultEvidence-backed prospecting
Operational resultHuman-reviewed drafts
Workflow scopeCRM-linked research

How the workflow runs

The workflow captures evidence first, normalizes it into CRM fields, drafts a message, and keeps a human between generated copy and live outreach.

  1. The workflow starts from CRM or target account data.
  2. The research layer reviews the website and public context.
  3. LinkedIn or other external evidence is captured alongside the record.
  4. Public datasets are checked where relevant.
  5. Simple qualification rules score or label the prospect.
  6. The engine prepares an outreach message for review.
  7. A human reviews the evidence and sends the message.

Human control

The control points were specific to the workflow, so the agent could speed up the work without silently taking over sensitive decisions.

  • The system drafts; humans send.
  • Evidence stays attached to the prospect.
  • Reviewers can see why a message was suggested.

What shipped

The implementation centered on these shipped pieces:

  • CRM enrichment workflow.
  • Website and LinkedIn research flow.
  • Public dataset checks where relevant.
  • Qualification rules.
  • Evidence capture for each prospect.
  • Outreach message preparation for review.
  • Follow-up queue for humans.
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