Lead Enrichment

Lead Enrichment

Takes a lead identifier (email, GitHub username, or L-### ID) and enriches their profile using every available data source. Updates the lead record in data/leads.json with enriched metadata, recalculated score, and recommended next action.

Dependencies

Load crm-identity first (for scoring rubric and ICP definitions).

When to Use

• When a new lead is added to data/leads.json
• When preparing for outreach to a specific lead
• When Ed asks to "look up" or "research" a lead
• When a lead's stage changes and we need updated context
• Before drafting any email to a lead (always enrich first)

Enrichment Sequence

Run these 7 steps in order. Each step may inform the next.

Step 1: Identify & Load Lead

Look up the lead in /var/www/html/systemprompt-web/reports/crm/data/leads.json by email, github_username, or L-### ID. Record the current state before enrichment.

Step 2: Email Domain Analysis

Extract the domain from the lead's email address. Classify it:

If corporate domain, the company domain is the enrichment anchor for Steps 5-6.

Step 3: Gmail Thread Search

Search for all email history with this lead using Gmail MCP:

gmail_search_messages: "from:{email} OR to:{email}"

If threads are found, read the most recent 3 threads with gmail_read_thread. Extract:

• Thread count — total email conversations
• Last email date — most recent contact
• Sentiment — positive/neutral/negative based on last message tone
• Key interests — what did they ask about or respond to?
• Objections — any concerns or reasons for hesitation
• Additional contacts — anyone CC'd or mentioned (potential related leads)
• Role/title — often in email signatures

Rules:

• Read-only. Never send, draft, or modify emails in this step.
• Summarise conversation context — do not quote email content verbatim in CRM notes.
• If no threads found, set email_context.sentiment to "no_contact".

Step 4: GitHub Profile (if github_username is set)

gh api users/{github_username}

Extract: name, company, bio, location, public_repos, followers, created_at

Check recent activity:

gh api "users/{github_username}/events?per_page=10"

Look for:

• systemprompt-related activity (stars, forks, issues)
• MCP/Claude repos starred or forked
• AI/governance-related repos
• Recent commit frequency (active developer vs dormant)

Also check for interactions on systemprompt repos:

gh api search/issues -f q="repo:systempromptio/systemprompt-template author:{github_username}"
gh api search/issues -f q="repo:systempromptio/systemprompt-core author:{github_username}"

Step 5: Web Research

Run up to 3 targeted web searches per lead. Only search if company is known or inferable.

Search 1 — Person + Company:

WebSearch: "{full_name} {company_name}"

Look for: LinkedIn profile URL, role confirmation, conference talks, blog posts, public GitHub presence.

Search 2 — Company + AI/Tech:

WebSearch: "{company_name} AI governance" OR "{company_name} Claude Code" OR "{company_name} MCP"

Look for: company's AI adoption, existing tools, public statements about AI strategy.

Search 3 — Company profile (if needed):

WebSearch: "{company_name} company size employees founded"

Look for: headcount, funding stage, founding year, industry vertical.

Rules:

• Never scrape LinkedIn. If a LinkedIn URL is found in search results, save the URL as web_research.linkedin_url for Ed to check manually.
• Attribute every data point to its source.
• If searches return nothing useful, note "no public presence found" and move on.

Step 6: Company Website

If a corporate email domain was identified in Step 2, fetch the company homepage:

WebFetch: "https://{company_domain}"

Extract from the page content:

• Company description — what they do (one sentence)
• Industry vertical — e.g. "cybersecurity training", "food services", "ERP consulting"
• Size signals — team page, office locations, customer logos
• Technology signals — mentions of AI, Claude, MCP, automation, governance
• Geographic presence — headquarters, office locations

If the fetch fails or returns nothing useful, fall back to web search results from Step 5.

Step 7: Internal Cross-Reference

Read the full leads.json and check:

1. Same-company leads — find other leads with matching company field or email domain. Record their IDs in related_leads.
2. Shared tenants — if lead has a tenant_id, find other leads on the same tenant.
3. Interaction history — read interactions.jsonl filtered by lead_id. Summarise total count, last date, types, direction balance.
4. Email log — read email-log.jsonl filtered by email. Note templates used, reply status.
5. Deal association — check deals.json for deals linked to this lead's ID or company.

Enriched Data Schema

After enrichment, the lead's enriched_data field should contain:

{
  "company_size": "100+|31-100|11-30|1-10|unknown",
  "role": "CTO|Senior Engineer|etc",
  "location": "city, country",
  "icp_fit": "high|medium|low|unknown",
  "tenant_id": "uuid or null",
  "tenant_active": true/false/null,
  "company_profile": {
    "domain": "company.com",
    "description": "One-sentence company description",
    "industry": "Industry vertical",
    "headcount_estimate": "~200 employees",
    "tech_signals": ["Claude Code user", "MCP adopter", "AI governance interest"]
  },
  "web_research": {
    "linkedin_url": "https://linkedin.com/in/... (suggested, not scraped)",
    "public_mentions": "Brief summary of public presence",
    "last_researched": "2026-04-16"
  },
  "email_context": {
    "thread_count": 3,
    "last_email_date": "2026-04-02",
    "sentiment": "positive|neutral|negative|no_contact",
    "key_interests": ["governance", "white-label", "MCP security"],
    "objections": ["maturity concerns", "integration requirements"]
  },
  "related_leads": ["L-004", "L-005"],
  "enrichment_sources": ["gmail", "github", "websearch", "webfetch", "internal"],
  "enrichment_confidence": "high|medium|low",
  "last_enriched": "2026-04-16T12:00:00+00:00"
}

Scoring After Enrichment

Recalculate the lead's total score using all enrichment signals plus the rubric from crm-identity:

Output Format

Print a summary after enrichment:

## Lead Enrichment: {name} ({email})

**Score:** {old_score} → {new_score} ({+delta})
**Stage:** {stage}
**Company:** {company} — {company_description}
**Role:** {role/title}
**Industry:** {industry_vertical}
**Location:** {location}
**ICP Fit:** {High/Medium/Low} — {one-line reason}

### Data Sources Used
{list of sources that returned data: gmail, github, websearch, webfetch, internal}

### Key Findings
- {bullet list of new or updated data points, each attributed to source}

### Email Context
- Threads: {N} | Last contact: {date} | Sentiment: {sentiment}
- Interests: {comma-separated list}
- Objections: {comma-separated list or "none identified"}

### Related Leads
- {L-### name (company) — relationship}

### Interaction Summary
- Total: {N} | Last: {date} ({type})
- Direction: {N} inbound / {M} outbound

### Recommended Next Action
{One specific, actionable recommendation based on stage, score, sentiment, and recency}

Rules

1. Never fabricate data — if a field can't be determined, leave it null
2. Always attribute the data source — e.g. "CTO (from Gmail email signature)" or "~200 employees (from company website)"
3. LinkedIn: suggest URL, never scrape — save the URL for Ed to verify manually
4. Gmail: read-only, summarise — extract insights but do not quote email content verbatim in CRM data
5. Max 3 web searches per lead — be targeted, not exhaustive
6. Update updated_at timestamp on every enrichment
7. Log enrichment as an interaction of type enrichment in data/interactions.jsonl
8. Rate limit — max 1 full enrichment per lead per day (re-running same day overwrites, doesn't duplicate)
9. Batch mode — when enriching multiple leads, process sequentially and print a summary table at the end