Follow Up Faster with AI-Generated Email Sequences

Most follow-up email sequences are written once, approved once, and sent to everyone the same way. The SDR chasing a VP of Engineering at a 500-person SaaS company sends the same three emails as the one chasing a Marketing Manager at a 20-person startup. Same copy. Same timing. Same offer.

But the problem runs deeper than personalization. When Warmly audited our own outbound, we found emails going out that referenced pricing we no longer charged and features we had deprecated months earlier. The signal was right. The timing was right. The email itself was wrong, because no one had updated the template.

That's when it clicked: a generic email sent at exactly the right moment is not a neutral outcome. It's often worse than sending nothing at all.

High-intent moments are finite and they don't reset. A prospect who just visited your pricing page three times in one week is in a window. Send them a templated email that ignores everything they just did, and you don't just miss the moment. You spend it. They learn to tune you out. The next email, the next signal, the next touchpoint all starts from a lower baseline because you already showed them what your outreach looks like.

We spent four years building the signal chain: identifying who's on the site, what they're doing, what company they work for, where they are in the buying process, what signals third parties are seeing on them. All of that to send a template someone wrote six months ago.

So we built AI Emails on top of that signal chain. Every email is generated fresh from everything Warmly knows about that contact at the moment the trigger fires. No templates. No stale copy. No pricing from two product versions ago.

Use Cases

This step works across any trigger in Warmly. Here are four examples of how teams are using it:

• Inbound chat follow-up. A qualified visitor has a full conversation with your site bot but leaves without booking. The AI reads the entire chat transcript, sees what they asked about, what objections came up, and what was promised, then writes a follow-up that picks the conversation back up rather than starting from scratch. The contact gets an email that feels like it came from someone who was actually in the room.
• Intent trigger. Bombora flags a company showing a research surge around a topic like "chatbots" or "website conversion." Instead of sending a cold generic opener, the AI writes outreach that acknowledges they're actively evaluating, surfaces case studies from companies in their industry that solved the same problem, and frames Warmly as the obvious next step in their research.
• Job change. A champion at an existing account moves to a new company. They already know Warmly and what it does. The AI writes a warm intro at the new account that references the prior relationship, connects it to the new context, and opens the door without being heavy-handed about it.
• Social engagement. A buyer likes a competitor's post about website de-anonymization. They get an email about Warmly's identification rates, written to make the contrast direct and relevant to the signal they just showed.

What the agent knows before it writes

On every send, Warmly retrieves the full signal chain for that contact before generating anything:

• Chat transcript (when there is one): what was said, what was promised, what objections came up
• Every page visited during that session and any return visits
• Enriched contact data: name, title, seniority, role
• Enriched company data: industry, size, tech stack
• CRM context: lifecycle stage, open opportunities, prior ownership
• Any second or third-party intent signals already on the account (Bombora topics, hiring activity, news, and others)

The email is written from all of that, not from a template.

What you need before you start

• An orchestration with a trigger already set up (chat engager, form fill, intent signal, TAM audience, or any other)
• An Assign To step earlier in the orchestration so emails send from the right rep's mailbox
• A connected sending mailbox for those reps
• A connected CRM (HubSpot or Salesforce) so Warmly can pull in deal stage and ownership context

Step 1: Set your trigger and filters

Open the Outbound Agent and edit or create an orchestration. Configure your trigger and audience filters. A high-value starting segment: contacts where email captured is true, book a meeting was shown is true, and meeting was not booked. These are qualified chat engagers who got close but didn't convert.

Step 2: Add the Assign To step

Before adding the email step, make sure there's an Assign To step in the flow. This tells Warmly which rep's mailbox to send from, and it also means the AI can personalize using that rep's context and prior CRM activity with the account.

Step 3: Add Generate Personalized Sequence

After the Assign To step, add the Generate Personalized Sequence action. This is the AI step that builds a multi-touch email sequence for each contact individually.

Step 4: Write your Goal

The Goal tells the AI what the sequence is trying to accomplish. There's a default baseline, but add your own context to get better output. Example:

• Follow up with a qualified chat engager who didn't book a meeting. Reference what they discussed in chat. Keep each email under five paragraphs.

The more specific your goal, the tighter the output. Mention the persona, the action that triggered the sequence, and what a successful outcome looks like.

Step 5: Set your Policy

The Policy sets the guardrails. Think of it as the brief you'd give a great copywriter. Examples:

• Email 3 should be a two-sentence bump, not a re-pitch
• Do not mention pricing in email 1
• Always reference something specific from the chat conversation
• Use a casual tone, not formal

Step 6: Test and Refine

Click Test and Refine. Warmly generates three sample scenarios with complete email sequences. Review each one and give feedback in plain English directly in the interface. Examples:

• Make email three shorter. Just a two-sentence follow-up.
• The opener is too generic. Reference the pricing page visit.
• Good overall but shorten email one by half.

Hit regenerate after each round of feedback. Repeat until the output sounds like something you'd actually send. Once it does, click Exit and save.

Step 7: Activate

Save the orchestration and turn it on. Setup takes about five minutes. Every contact that enters this step from now on gets a uniquely generated sequence based on their full signal chain, not a template.

After implementing AI-generated sequences into Warmly's own signal orchestrator, reply rates went from 2% to 6%, a 3x improvement. The reason is straightforward: each email is built on the actual signal that brought that specific person into the orchestration. Not a template written for an imaginary average prospect.

Three things improve immediately:

• Speed: Follow-up goes out while the intent signal is still live, not hours later after a rep finishes writing. For chat engagers especially, timing is everything. They were on your site, they were engaged, the window is short.
• Relevance: The emails reflect what that specific contact actually did and said. A buyer who engaged with a competitor post gets a different email than a Bombora surge account, even if they're at the same company and both entered the same orchestration.
• Rep time: Ops writes the goal and policy once. The AI handles the rest. Reps stop spending 10 to 15 minutes per contact on follow-up drafts and use that time on conversations that are already moving.

The Test and Refine loop means you're not just hoping the AI gets it right. You shape the output until it meets your standard, then lock it in for every future contact that hits the same step.

Schedule a demo with our team to see our playbooks in action. Or, view more of our plays here.