Build a multi-agent sales email workflow

I didn’t build a sales team. I built an AI sales agent. And it’s brilliant. Let’s be real: most early-stage teams don’t fail because of bad products. They fail because they can’t scale outbound. We were on the edge of that cliff - until we changed our approach. Instead of hiring 3–5 SDRs, we built an AI-powered sales agent. Here’s how it works, and why it’s outperforming most human teams: 🧱 Step 1: AI-Powered CRM Assembly We asked GPT-4 to help us define exactly who we want to pitch to: ▪️Role titles ▪️Industry verticals ▪️Growth stage ▪️Signal triggers (hiring, funding, tech changes) Then, using Clay + Clearbit + Crunchbase, we trained the AI to: → Scrape relevant accounts → Verify them via email & LinkedIn → Enrich each record with role-specific context The CRM now refreshes itself. No data team. No weekly list builds. ✍️ Step 2: Auto-Personalized Outreach We trained ChatGPT on our: ▪️Offers ▪️Value props ▪️Case studies ▪️Brand voice Then layered on Relevance AI + Notion to: → Generate 1:1 intros per account → Segment CTAs based on role & stage → Preload first touches into Smartlead Every email feels hand-written. Even though it’s not. 🔁 Step 3: Multi-Channel Sequencing Our agent runs synchronized plays across: ▪️Email (Smartlead: rotating domains + automated warmups) ▪️LinkedIn (HeyReach.io: soft DMs, no spam) ▪️Retargeting (Custom Meta/Google audiences for touched accounts) We get 7–9 touches in per lead, with zero manual chasing. When a prospect clicks, replies, or connects: → Zapier tags them in HubSpot → AI routes them to our BDR (Jony) → Slack pings us to jump in if needed ☕ Step 4: Human Handoff for Qualified Leads This is where it shines. The agent doesn’t just book random meetings. It nurtures, qualifies, and engages before we step in. When Jony or I jump into a call, the prospect already: ▪️Knows who we are ▪️Has read 2–3 posts ▪️Has seen our offer breakdown ▪️Is ready to explore implementation We don’t pitch. We prescribe. 📈 Results So Far ▪️Weekly outreach capacity: 4,000+ contacts ▪️Reply rate: 12% ▪️Booked meetings: 35+ per month ▪️Time spent prospecting manually: Zero ▪️Headcount added: None We’re closing more, talking to better-fit buyers, and spending more time on calls - not chasing them. Final Thought: Everyone’s trying to scale with headcount. We’re scaling with systems. AI doesn’t replace the relationship. It just clears the path so your team can build it faster. 💬 Want our full playbook on how we built this AI agent? Comment “AI SDR” and I’ll DM you the doc with: 🔹Tool stack 🔹Prompts 🔹Workflows 🔹CRM logic 🔹Metrics to track No more spray-and-pray. It’s time to engineer your GTM. #GTM #RevOps #AISales #OutboundAutomation

RevOps folk spend too much time firefighting 🚒 But automation isn't always a simple solution...especially when you need to handle the messy unstructured context of deal progressions. Here's how I built a workflow using Tango's new CRM Admin agent and "hybrid automation" to save reps a ton of time AND enforce good data hygiene and business rules in HubSpot. Here's how it works: 1. Extract the economic buyer from an email ✨ Agent reviews and extracts relevant information 🙋♂️ Rep validates the extracted information is correct 2. Verify contact on LinkedIn ✨ Agent searches for the economic buyer on LinkedIn and extracts job title 🙋♂️ Rep validates the extracted information is correct 3. Update deal contacts in HubSpot ✨ Agent searches for the deal record using the company name from the email ✨ Agent adds the new stakeholder to the deal record ✨ Agent applies correct association labels to the contact-deal relationship 4. Extract deal context information ✨ Agent extracts context from deal record (notes, call transcripts, emails) ✨ Agent fills required fields to progress the deal ✨ Agent assigns a deal score with explanation and rationale ✨ Agent searches identifies potential risks and next step suggestions 🙋♂️ Rep can edit any information and override rules by providing rationale 5. Progress the deal ✨ Agent fill required information in the deal progression form 🙋♂️ Rep saves the information and moves forward 6. Create follow-up tasks automatically ✨ Agent generate appropriate next steps 🙋♂️ Rep confirms details and completes the workflow The workflow turns unstructured data from prospect/customer conversations into structured and validated data in HubSpot, ensuring accurate forecasting and saving reps a ton of time on CRM busywork. #tangopartner

We’ve been building powerful AI agents and workflows across every part of our sales process — here are some of our favorites: 📨 Automate daily meeting prep – Each morning, an AI assistant emails the team a sales meeting agenda, complete with attendee insights, past interactions, and talking points for every call. 📞 Compile meeting minutes to create a call query chatbot – All calls are transcribed and saved for this assistant to reference, so we can ask questions, get summaries, and draft informed follow-up emails in seconds. 🤝 Identify decision-makers on new leads – When a new lead arrives, an AI research agent pinpoints the key people from the company for us to be in contact with, providing us with their LinkedIn profile and email automatically. 📈 Draft cold emails using deep company research – AI finds and pulls data from company 10-Ks to create personalized, high-impact emails for executives and founders. 👨💻 Enrich leads + send personalized emails – When a lead fills out our website form, this workflow enriches their contact in our CRM and instantly drafts them a hyper-personalized intro email. 📑 Answer RFP questions in bulk – AI reads RFPs in any format and automatically generates responses based on our past answers and company knowledge.