Building an AI Sales Copilot That Increased Qualified Pipeline by 3.2×

The Challenge

A B2B SaaS company with a 35-person sales team was struggling with pipeline quality. SDRs spent 3+ hours daily on research, data entry, and writing cold outreach. Conversion from lead to qualified opportunity sat at 8.3%.

What We Built

A native Salesforce Lightning Web Component that surfaces an AI copilot panel inside every lead and opportunity record.

Feature 1: Lead Enrichment Agent

On lead creation, an enrichment agent automatically researches the company using web tools and LinkedIn data, populating 40+ fields and writing a one-paragraph "Why Now" brief for the SDR.

Feature 2: Hyper-Personalized Outreach

SDRs click "Draft Email" and receive 3 email variants, each personalized to the prospect's:

• ▸Recent company news
• ▸LinkedIn activity
• ▸Industry pain points
• ▸Technology stack (inferred from job postings)

// Salesforce LWC calling our AI backend
const draftOutreach = async (leadId: string, variant: 'cold' | 'followup' | 'breakup') => {
  const lead = await fetchLeadContext(leadId)
  const response = await fetch('/api/ai/draft-email', {
    method: 'POST',
    body: JSON.stringify({
      lead,
      variant,
      model: 'claude-3-5-sonnet-20241022',
      tone: userPreferences.tone,
    })
  })
  return response.json()
}

Feature 3: Deal Intelligence

For open opportunities, the copilot surfaces:

• ▸Win probability score (updated daily via ML model)
• ▸Recommended next action with reasoning
• ▸Risk signals ("No activity in 14 days — deal may be stagnating")
• ▸Comparable won deals with similar profiles

Integration Architecture

The backend is a FastAPI service deployed on AWS Lambda with a Salesforce Connected App for OAuth. We use Claude for natural language generation and GPT-4o for structured data extraction and classification (better JSON reliability for our use case).

Results After 6 Months

| Deals closed (team total) | 38 | 91 |