Lead Intel Studio

Lead Intel Studio is a starter app for a custom lead-research and outreach system. It is designed to ingest contacts from a CSV export today, add Salesforce import next, enrich each account and prospect, classify the buying committee, and produce reviewed outreach drafts for Gmail or Dripify workflows.

Why this approach

This should be a normal application, not an OpenClaw-first automation stack.

• CRM data, message approvals, and outbound controls benefit from explicit roles, logs, and database records.
• The AI layer should be swappable so you can use OpenAI, Anthropic, or both depending on cost and quality.
• Dripify works well as an execution channel, but the core system of record should stay in your app.

Current MVP contents

• A dashboard in app/page.tsx
• A sample JSON blueprint route in app/api/blueprint/route.ts
• CSV parsing logic based on your export shape in lib/csv.ts
• Domain types for contacts, research, and drafts in lib/types.ts
• Heuristic grouping and classification logic in lib/pipeline.ts
• Starter research and messaging prompts in lib/prompts.ts

Recommended production architecture

1. Ingestion
   • CSV upload for lead exports and account exports.
   • Salesforce OAuth app plus REST API for smaller syncs and Bulk API 2.0 for larger pulls.
2. Data model
   • accounts
   • contacts
   • research_runs
   • source_evidence
   • buying_groups
   • message_drafts
   • campaigns
   • engagement_events
3. Research pipeline
   • Normalize records.
   • Resolve account domain and LinkedIn identity.
   • Gather company and prospect facts with evidence links.
   • Classify organization type and buying group.
   • Generate outreach messaging from approved playbooks.
4. Execution
   • Gmail draft or send support with approval states.
   • Dripify CSV export so the sequencing engine stays consistent with your LinkedIn workflow.
   • Salesforce task and note writeback.
5. Feedback loop
   • Capture opens, replies, booked meetings, and opportunities.
   • Score which combinations of title, organization type, opener, and CTA are converting.

Suggested next implementation steps

1. Add database storage and auth.
2. Replace heuristic enrichment with a queued worker that calls the model provider and web research provider.
3. Add Salesforce OAuth and contact sync.
4. Add Gmail draft creation and send approval.
5. Add a Dripify CSV formatter for campaign uploads.

Setup

1. Install dependencies:

pnpm install

2. Start the app:

pnpm dev

3. Open http://localhost:3000.

Current recommendation on tooling

• Best core build choice: custom app with provider-swappable AI orchestration.
• Best AI setup: keep OpenAI and Anthropic both available behind a common interface.
• Best outreach execution mix: Gmail for direct email drafts and Dripify for LinkedIn-centric sequencing.
• Best immediate input path: CSV first, then Salesforce sync once schema and approval flow are settled.

Important guardrails

• Store evidence links for every enrichment claim.
• Separate confirmed facts from model inference.
• Keep a human approval step before first-touch sends.
• Rate-limit outreach and monitor bounce or spam signals.
• Keep compliance review in scope for CAN-SPAM, privacy, and platform usage policies.