05 · W2 — Groq Enrichment Pipeline

W2 runs on a scheduled trigger and processes all prospects in diagnostic_pending status that have not yet been enriched by Groq. For each prospect, it assembles all available data — company, domain, website URL, tech stack, tech gaps, LinkedIn URL, name, title — and sends it to a Groq agent node.

The Groq Enrichment Prompt Structure

The prompt instructs Groq to produce a structured JSON object containing exactly these fields:

Field	Type	Description
`icp_score`	0–100	Based on company size signals, industry fit, geography, decision maker title, and distress signal presence
`enrichment_summary`	String	3–5 sentences describing the company, their likely pain points, and why they fit ANR
`ux_flaws`	Array	Specific UX problems Groq can infer from tech stack, snippet, and company type
`missing_automations`	Array	Automation gaps based on BuiltWith tech stack data
`recommended_anr_services`	Array	Mapped ANR services based on identified gaps
`bait_finding`	String	Preliminary high-specificity finding suggestion for Claude to validate or replace

What W2 Does After Groq Runs

Writes a diagnostic row to outreachiq_diagnostics linked to the prospect_id
Updates the prospect status to diagnostic_pending in outreachiq_prospects
The diagnostic row contains the full Groq JSON alongside the prospect's raw data
This row is what the Diagnostics Queue screen in the dashboard reads

Groq's Role and Limitations in W2

Groq is fast and cheap — processing 25 prospects per week at effectively zero cost. Its job in W2 is to produce a first-pass hypothesis that gives Claude a starting brief to either validate or override.

What Groq does well in W2:

ICP scoring from structured data signals
Tech gap identification from BuiltWith output
Enrichment summaries from company name, industry, and snippet
Preliminary bait finding suggestions based on industry patterns

What Groq gets wrong in W2: