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Your Project Review

A clear scope and recommended approach for your specific project.

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Capaion Project Review — Sample · Solo Founder
Solo Founder · Lead Generation Automation · Intermediate
1

Recommended Approach

Claude 3.5 Sonnet (Anthropic)

Claude 3.5 Sonnet is the highest-leverage primary model for your use case. It handles complex reasoning, long-context processing, and structured output generation — all critical for a lead generation system that needs to qualify, personalize, and respond intelligently at scale. Its API reliability and instruction-following accuracy make it the safest anchor for a production workflow.

2

Tools & Technology Required

Claude 3.5 SonnetCore AI Engine

Primary intelligence layer — lead scoring, personalized outreach copy, and qualification logic.

Make (Integromat)Automation Platform

Orchestrates the full workflow — triggers, data routing, and API calls between every system.

Apollo.ioLead Data Source

Enriched contact and company data piped into the AI layer for context-aware outreach.

Instantly.aiOutreach Execution

Handles email sequencing, deliverability, and reply detection at scale.

AirtableCRM & Data Store

Central database for lead records, AI outputs, and pipeline status tracking.

SlackAlerts & Monitoring

Real-time notifications when high-intent leads reply or hit qualification thresholds.

3

What Gets Connected

1

Apollo → Make

Apollo exports enriched lead lists on a schedule. Make receives them via webhook and begins the qualification sequence automatically.

2

Make → Claude API

Make sends each lead's profile data to Claude with a structured prompt. Claude returns a lead score, qualification flag, and a personalized opening line.

3

Claude → Airtable

AI outputs are written back to the lead's Airtable record — score, reasoning, and personalized message stored against the contact.

4

Airtable → Instantly

Qualified leads (score ≥ 7) are automatically pushed into an Instantly sequence with their Claude-generated first line pre-loaded.

5

Instantly → Slack

When a lead replies or books a call, Make triggers a Slack alert with full context — no manual inbox monitoring required.

4

Expected Outcomes

↗ Find high-intent leads without manual research

Apollo enriches and filters contacts automatically. Make runs the qualification loop — no spreadsheet work.

↗ Send personalized outreach at scale

Claude generates a unique, context-aware first line for every contact based on their role, company, and industry.

↗ Know which leads are worth your time

Claude scores each lead 1–10 with reasoning. Only leads scoring 7+ enter the outreach sequence.

↗ Keep everything organised without a full CRM

Airtable acts as your lightweight CRM — automatically updated by Make as leads move through the pipeline.

5

Implementation Timeline

Week 1

Foundation

Set up Claude API access, Airtable base, and Make account. Build the core Claude prompt and test lead scoring on 20 sample contacts.

Week 2

Integration

Connect Apollo export to Make. Wire the Make → Claude → Airtable pipeline. Run end-to-end test with live data and validate output accuracy.

Week 3

Outreach Layer

Connect Airtable → Instantly. Configure email sequences with Claude-generated first lines. Set up Slack alerts and run a 50-lead pilot.

Week 4

Optimize & Scale

Review pilot results. Refine the Claude scoring prompt based on reply rates. Increase volume to 200+ leads/week. Full system live.

6

Your Next Steps

01Get Claude API access

Create an Anthropic account, generate an API key, and run a test call to confirm your prompt returns valid JSON. Budget: ~$20–40/month at your expected volume.

02Build your lead scoring prompt

Craft a system prompt that takes a lead's job title, company size, industry, and bio and returns: score (1–10), reasoning, and a personalized first line. Test on 10 real contacts before automating.

03Set up your Airtable base

Create a Leads table with fields for Apollo data, AI score, AI reasoning, personalized line, status, and reply flag. This is the single source of truth for your pipeline.

04Build the Make scenario

Create a scenario: watches for new Apollo exports → sends each lead to Claude → writes AI output to Airtable → checks if score ≥ 7 → pushes qualified leads to Instantly.

05Run a 50-lead pilot

Before scaling, run 50 leads through the full system. Check that scores feel accurate, first lines sound natural, and Airtable is updating correctly. Fix prompt issues here.

06Scale and monitor

Once the pilot validates, open the floodgates. Set Apollo export to run daily. Monitor reply rates weekly — refine your Claude prompt if open rates dip below 30%.

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02

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03

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04

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