How to generate bid request emails with AI

Last week I posted about my outbound sales motion for SCALIS and how I booked more than 50 demos in less than 7 weeks, testing two different outreach platforms, Apollo.io and 11x, but I only did a deep dive on the framework I set up for Apollo (link in comments). Here's the process I followed for 11x: 1️⃣ Automated Email Address Creation: Unlike Apollo, where I had to buy additional domains and manually create email addresses, 11x.ai uses OAuth to connect directly to my account via IMAP/SMTP. This allowed 11x to automatically generate over 30 email addresses, such as brandon@yourscalis.com and brandon@meetscalis.com, without requiring GSuite or extra domain purchases. 2️⃣Email Warm-Up: Instead of relying on a separate tool like Mailreach for email warm-up, 11x.ai handles the entire warm-up process automatically. It manages the deliverability of emails behind the scenes, freeing up time and reducing the complexity of the setup. 3️⃣ Targeting: Similar to Apollo, I still had to create targeted searches based on my Ideal Customer Profile (ICP). For example, I built lists of hiring managers or people ops professionals at US-based companies using Lever and hiring for 4 or more roles. This ICP alignment remained consistent across both systems. 4️⃣A key differentiator with 11x.ai is its "Set an Offer" functionality. I provided the system with detailed information about SCALIS, including: - What I was selling (our AI-based hiring platform) - The pain points I was addressing (such as inefficiencies in existing hiring solutions like Lever) - SCALIS’ value propositions and proof points - The personality and tone I wanted to evoke in my messaging - The desired call-to-action (book a demo) Based on this input, 11x.ai auto-generated a series of highly personalized email and LinkedIn messages for each recipient. The messaging was tailored to the contact and the company they worked at, leveraging data it gathered about their business. 5️⃣ Sequence Activation: Once I reviewed the auto-generated messages, I selected which email addresses to use for outreach, set the number of leads 11x would source per day, and determined the days and times the messages would be sent. I then activated the sequence, and 11x took care of the rest. 11x.ai’s automation saved me domain management, copywriting, and email warm-up time. The platform was more hands-off but still delivered highly personalized outreach at scale, which led to consistent engagement. Ultimately, both systems contributed to the 50+ demos I booked in under 6 weeks. Apollo was better for manually tailoring my outreach when I needed a human touch, while 11x.ai excelled at automating repetitive tasks without compromising personalization. For anyone running outbound campaigns at scale, experimenting with different tools and systems can yield incredible results. The key is to balance automation with personalization and to stay consistent in your outreach efforts.

Here's a Clay generated AI sentence that 3x'd our positive response rates for a SaaS company selling to a very niche industry. We started sending this line in email 2 as a follow up but it was working so well we moved it into email 1 and it performed even better. All we did was use AI to create a comparison between what the company we were reaching out to does and how my customer helps people. So basically each line would look something like Hey {{First_name}}, I was on the site and saw how you help people save time with {{X}} like how we can help you save time with {{Y}} so I wanted to get connected. X in this case outlines how the company helps their customers and Y talks about how we can help them. The way we create the line is we use Clay and train a prompt on how we can help a company. Every company comes down to 5 main offers. Help people save time, save money, make more money, live longer, or increase their social status. For B2B, we are mainly concerned with the first 3 so we train the AI on how our company or our customer's company helps people in those 3 ways. So when AI evaluates that company, we can write a line about how we both help people save time or we both help people make more money etc. Then we manually write lines to train the AI on how we can specifically help them. This is the most crucial part of the line. The beginning is just a parlor trick but this part will really stick out our relevance to their company. We want to train AI to be able to apply what we do as a company to the sentence but we have to do it manually so that it gets as specific as we are looking for. This workflow has been in the campaign in email 1 for 6 months and nothing else has beaten it. Although, I think this workflow works well in this context because we aren't targeting people that get a ton of cold email usually so perhaps it's a tactic they haven't seen before. No reason it wouldn't work on Founders I don't think, I just can't say that we've done it and want to make sure I mention that. Just to give transparency if you want to try it yourself.

Most email outreach fails for one reason: It sounds exactly like what your competitors send to your prospects. Most "personalized" emails still sound generic and flat. And your prospects? They can smell the copy-paste from a mile away. So here’s how we flipped the script using real personalization with AI — and it actually works. Picture this: You want to send an email that feels like you actually know the person reading it. But you also want to scale it across dozens (or hundreds) of leads. Here’s the step-by-step playbook we use at Level: 1. Start with your CRM- Pull real data from your client or prospect profiles. 2. Use Browser AI to research- Scrape their website to find language, priorities, and cultural cues. 3. Grab their LinkedIn insights- Who are they? What are they posting? What matters to them? 4. Build a real persona- Use the "Jobs To Be Done" framework to define their motivations and pain points. 5. Map your services- Clearly list what you offer and identify case studies that match. 6. Ask ChatGPT to matchmake- Feed GPT the persona + services + case studies and ask what fits best. 7. Write the email with GPT-4.5- Give it a clear tone direction, include the inputs above, and let it draft a personalized message. The result? An email that actually feels like it was written just for them. No spray-and-pray templates. Just smart, human-centered messaging, at scale. This approach changed the game for us. It's faster, more relevant, and makes every lead feel seen. #AI #Personalization #B2BMarketing #MarketingInnovation #ChatGPT #Entrepreneurship #Leadership #AIMarketing

Here’s a tactical blueprint for using AI to write killer emails: Interview 5 customers. Ask what they were doing before, what they hated, and what pushed them to act. Have a notetaker in the call. Keep peeling the onion - act like a therapist and get them to talk as much as you can for the transcriber. Take the transcripts and prompt your favorite LLM as follows: “You’re a senior outbound strategist helping a founder translate raw customer interviews into a friction-first cold email. You have 5 transcripts of real buyer conversations. From these, extract the emotional spikes, recurring complaints, and phrases that signal urgency or frustration. Write a sequence of 3 cold emails that feel like they came from someone who lives in their world. It should: 1) Lead with a pain the buyer actually said, not what we think they feel 2) Be <60 words 3) Avoid adjectives, features, and startup fluff 4) End with a low-commitment ask (e.g. “Does any of that sound like your version of reality?”) Bonus: make it sound so specific it couldn’t have been written by anyone who hasn’t lived their pain.” Test them. Track responses. Keep what hits. Iterate the rest. Until your cold email mirrors a real complaint your customer muttered under their breath last week… you’re still guessing.

I've spent an embarrassing amount of time arguing with ChatGPT 😅 "No, that's not what I meant." "Try again." "More conversational." "Okay but like... better." Turns out, the problem wasn't (100%) the AI. It was me not knowing how to actually prompt the thing 😆 We all know the type of prompts that DON'T work... "Write an email to a [prospect] about our product." You hit enter, AI spits something out, and it's... fine? Generic? Sounds like every other AI-generated email your prospects are already ignoring? After watching hundreds of marketers (and myself) struggle with this, Corrina Owens and I built a framework that actually works: Signal Context + AI Role + Job + Output Format + Tone Here's what each piece does: 🚦 Signal Context: What are we reacting to? (competitor mention, asset download, re-engaged champion) 👤 AI Role: Who is the AI acting as? (AE, CMO, data analyst) Job: What are we asking it to do? (write email, analyze patterns, generate copy) 📜 Output Format: What structure do we want? (3-paragraph email, bullet list, Slack message) 📣 Tone: What's the emotional nuance? (confident, consultative, direct) Real example from our guide: ❌ Bad prompt: "Write an email to a prospect about {{feature of our product}}" ✅ Good prompt: "You're an AE who specializes in competitive displacement. A prospect just downloaded our guide comparing us to [Competitor]. Write a 3-paragraph follow-up email that's confident and consultative, addresses the most common objections we hear about [Competitor], and ends with a specific meeting CTA to discuss their evaluation criteria." The difference? The second prompt gives AI actual context about buyer behavior, defines the role clearly, and specifies the exact output you need. This is just one of six prompting modes we break down in our AI Prompt Guide. We cover everything from multi-signal account alerts (the prompts that generate Slack-ready messages for your reps) to win-loss pattern analysis (turning 6 quarters of deal data into predictive account scoring). Copy the prompts. Adapt them to your buyers. Stop arguing with ChatGPT 😉 https://lnkd.in/gxFAEAsi