AI Sales Outreach System
Researches, qualifies, drafts, and follows up at scale. Without sounding like a bot.
Overview
A pipeline of agents that turns a target list into personalized outreach. Each prospect is researched, scored, paired with a tailored opener, and queued into your sending tool. Humans approve, agents execute.
Architecture
Code shape
1const runOutreach = pipeline({2 enrich: (lead) => apollo.find(lead),3 score: (lead) => icpScorer.grade(lead, icp),4 draft: (lead) => draftAgent({5 model: "claude-sonnet-4",6 tone: brand.tone,7 evidence: lead.researchNotes,8 }),9 send: (msg) => smartlead.queue(msg, { afterApproval: true }),10});Illustrative. Actual implementation varies per integration. Code ships in your GitHub org.
Who it's for
- →B2B teams running outbound with 1–3 SDRs
- →Founders doing GTM themselves
- →Agencies running outbound for clients
Outcomes
- ✓5–10× the personalization volume per SDR-hour
- ✓Reply rates 3–5× generic blasts (typical 5–15% in our deployments)
- ✓Visibility into every step (logged, scored, improvable)
Capabilities
- ·Lead enrichment from public web + your CRM
- ·Scoring agent that ranks fit on your ICP
- ·Personalization agent that drafts opener + follow-ups
- ·Compliance pass for CAN-SPAM, GDPR, CASL
- ·Approval queue for human review before send
Stack
- ▸Anthropic Claude for drafting and reasoning
- ▸Apollo / Clay / custom scrapers for enrichment
- ▸Smartlead or Instantly for sending
- ▸PostgreSQL + a thin admin UI for review queue
- ▸Observability via Langfuse or custom
FAQ
Won't AI-written email get flagged as spam?
Spam filters target patterns of bulk un-personalized sends. Our system writes per-prospect copy referencing real specifics, sends from warmed-up domains at human pace, and respects all unsubscribes. Deliverability typically lands above 95%.
Can a human still approve every send?
Yes, that's the default. We also support fully autonomous mode for trusted flows once eval data justifies it.
Want a tailored scope for this engagement?
20 minutes on a call. We'll walk through your specific environment, integrations, and constraints, and follow up with a fixed-fee proposal.