By Erica Agrodnia

When I first joined HubSpot’s Conversational Marketing team, most of our website chat volume was handled by humans. We had a global team of more than a hundred live sales agents — Inbound Success Coaches (ISCs) qualifying leads, booking meetings, and routing conversations to sales reps. It worked, but it didn’t scale.

Every day, those ISCs fielded thousands of chat messages from visitors who needed product info, had support questions, or were just exploring. While we loved those interactions, they often pulled focus from high-intent prospects ready to engage with sales.

We knew AI could help us work smarter, but we didn’t want another scripted chatbot. We wanted something that could think like a sales rep: qualify, guide, and sell in real-time.

That’s how SalesBot was born — an AI-powered chat assistant that now handles the majority of HubSpot’s inbound chat volume, answering thousands of chatter questions, qualifying leads, booking meetings, and even directly selling our Starter-tier products.

Here’s what we’ve learned along the way.

How We Built SalesBot and What We Learned

1. Start with deflection. Then, build for demand.

When we first launched SalesBot, our primary goal was to deflect easy-to-answer, low sales intent questions (example: “What’s a CRM” or “How do I add a user to my account”). We wanted to reduce the noise and free up humans to focus on more complex conversations.

We trained the bot on HubSpot’s knowledge base, product catalog, Academy courses, and more. We are now deflecting over 80% of chats across our website using AI and self-service options.

That success in deflection gave us confidence, but it also revealed our next challenge. Deflection alone doesn’t grow the business. To truly scale value, we needed a tool that does more than resolve — it has to sell.

2. Use scoring conversations to close the gap.

Once we introduced deflection, we noticed a drop-off in medium-intent leads — the ones that weren’t ready to book a meeting but still showed buying signals. Humans are great at spotting those moments. Bots aren’t … yet.

To close that gap, we built a real-time propensity model that scores chats on a scale of 0–100 based on a blend of CRM data, conversation content, and AI-predicted intent. When a chat crosses a certain threshold, it’s raised as a qualified lead.

That model now helps SalesBot identify high-potential opportunities — even when a customer doesn’t explicitly ask for a demo. It’s a perfect example of how AI can surface nuance at scale.

3. Build to sell, not just support.

Once we’d nailed the foundations of deflection and scoring, we turned our attention to something bolder: turning SalesBot into a true selling assistant.

We trained it on our qualification framework (GPCT — Goals, Plans, Challenges, Timeline), enabling the bot to guide prospects toward the right next step: whether that’s getting started with free tools, booking a meeting with sales, or purchasing a Starter plan directly in chat.

Now, we have a tool that doesn’t just respond — …read more

Source:: HubSpot Blog

Author: Aaron