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The AI SDR Playbook
The exact architecture, tools, and workflows we use to deploy AI agents that replace manual outbound and book meetings 24/7.
By Frederic de Lavenne - Founder, The AI Pipe
The exact architecture, tools, and workflows we use to deploy AI agents that replace manual outbound and book meetings 24/7.
By Frederic de Lavenne - Founder, The AI Pipe
Most people think an "AI SDR" is a chatbot that sends emails. It's not. A real AI SDR is a multi-component system where each part handles a specific job. Here's the architecture we deploy for clients:
The key insight: each layer is independent. You can swap tools at any level without rebuilding the whole system. This is what makes it maintainable at scale.
There are hundreds of AI sales tools. Most are noise. Here are the ones that survive real-world deployment:
Dedicated cold email platform with built-in warmup, rotation across multiple inboxes, and deliverability monitoring. This is not Gmail.
Waterfall enrichment across 3+ data providers. One source is never enough. Cross-reference to get verified emails, company data, and technographics.
Claude or GPT-4 for prospect research and message generation. The model matters less than the prompt architecture. We use structured output with guardrails.
Real-time tracking of open rates, reply rates, and meeting conversion. Every email is a data point that improves the next batch.
Pro tip: Don't try to build everything from scratch. The best AI SDR systems are assembled from best-in-class tools at each layer, not monolithic platforms that do everything poorly.
This is the exact sequence we follow when deploying an AI SDR system for a client. Skip a step and the whole thing underperforms.
"B2B SaaS companies" is not an ICP. "Series B+ SaaS companies in fintech with 50-200 employees, currently using Salesforce, who posted a VP Sales job in the last 90 days" is an ICP. The AI is only as good as the targeting.
Pull prospects from multiple sources. Enrich with company revenue, tech stack, recent funding, job postings, and news. This data feeds the personalization engine. Without it, you're just sending spam with a robot.
This is where 90% of DIY attempts fail. You need a structured prompt that takes enrichment data as input and generates emails that sound like a human who actually researched the prospect. We use a 3-layer prompt: persona, context injection, and output constraints.
Cold email without proper infrastructure lands in spam. Period. We use 5-8 dedicated sending domains with gradual warmup over 2-3 weeks before any campaign touches a real prospect. Inbox rotation, SPF, DKIM, DMARC - all non-negotiable.
The first batch is never perfect. We monitor reply rates daily, A/B test subject lines and opening hooks, and feed winning patterns back into the prompt. By week 3, the system is outperforming most human SDR teams.
Here's an example of what our AI SDR agents produce. Notice: it's not a generic template with {first_name} placeholders. Every line references real data about the prospect.
Subject: {company_name}'s {recent_initiative} {first_name}, Saw that {company_name} just {trigger_event}. When {similar_company} was at a similar stage, their outbound team was spending 6+ hours/day on manual prospecting. They cut that to zero. The short version: an AI agent now handles prospect research, personalized outreach, and follow-ups across their entire pipeline. Their AEs went from 8 meetings/month to 22, without adding headcount. Worth a 15-min call to see if this fits {company_name}'s growth plans? Best, {sender_name}
Subject: Re: {company_name}'s {recent_initiative} {first_name}, quick follow-up. I put together a 2-min breakdown of how {similar_industry} companies are using AI agents for outbound right now. The TL;DR: - 3x more prospects contacted per week - 60-70% open rates (vs. 15-20% industry avg) - Human reps freed up to close, not chase Happy to share if useful. No pitch attached. {sender_name}
Key principle: The variables in orange are filled by the AI research agent, not from a static CSV column. That's the difference between "automation" and "intelligence."
After deploying across multiple clients, here are the benchmarks an AI SDR system should hit within 30 days of going live:
If you're below these numbers, the problem is usually in one of three places: targeting (wrong ICP), deliverability (emails hitting spam), or messaging (generic prompts). Fix them in that order.
We've audited dozens of failed AI SDR deployments. The same mistakes keep showing up:
Sending from your main domain. One spam complaint and your entire company email reputation is toast. Always use dedicated sending domains that are separate from your primary business domain.
Skipping warmup. Sending 500 emails from a fresh domain on day 1 is a guaranteed trip to spam. Warmup takes 2-3 weeks minimum. There are no shortcuts.
Using one data source. No single provider has accurate data for more than 60-70% of contacts. Waterfall enrichment across 3+ sources is essential for verified emails.
Generic prompts. Telling the AI to "write a cold email to this person" produces garbage. You need structured prompts with persona definition, tone constraints, length limits, and explicit instructions on what data to reference.
No human in the loop. AI SDRs work best as autonomous systems with human oversight on replies. Let the AI prospect and send, but have a human review positive replies before booking meetings. Remove this step only after 30+ days of proven quality.
We deploy done-for-you AI SDR systems for B2B companies. You get the meetings, we handle the infrastructure.
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