Your Chatbot Isn’t Dumb. It’s Just Blind.

Your Chatbot Isn’t Dumb. It’s Just Blind.

How we’re wiring real-time session data into chat to actually help users - without the fluff.

A few weeks ago, I was using Rippling. I just wanted to see how much we’d paid a contractor over a specific date range. Simple enough.

I clicked around.

Hovered over a few menu items.

Went into one tab, backed out.

Typed something in search.

Tried a different flow.

Backed out again.

After five minutes of getting nowhere, I gave up and asked the chatbot.

But here’s the thing; it asked me to explain what I was trying to do. From scratch.

Even though the product had just watched me struggle in real time.

It knew where I clicked.

It knew what I hovered over.

It knew I was stuck.

This isn’t a Rippling problem. Most chatbots work this way.

They’re disconnected from the session, so they’re disconnected from the user.

And yet, all the signals are there. Every hover, click, hesitation, backtrack. It’s already happening in the session data. But chat keeps acting like it’s starting from zero.

That’s what we’re fixing with Autoplay Chat.

What Autoplay Chat actually does

Autoplay Chat connects directly to live session data, so when someone’s stuck, we don’t need to guess.

It notices when a user hovers over the same button a few times.

Or keeps opening and closing a dropdown.

Or clicks “Save” and nothing happens.

Instead of sitting there passively, it steps in at the right moment, with the right context.

Not with “Need help?”

But something like: “Looks like you’re trying to filter by date, want a hand?”

We started with unsupervised techniques; no labeling, no manual tagging. Just real signals from real usage.

• Struggle detection
  
  Repeated hovers, input corrections, toggling, failed clicks - patterns any human watching would flag as frustration.
  
  

• Heatmap prompts
  
  If most users fly past a button but one person keeps hovering, we surface something useful right there.
  
  

• Proactive messaging
  
  We detect short loops (e.g. dropdown → back → dropdown) and nudge the user while they’re stuck, not after they leave.
  
  

• Session clustering
  
  We convert sessions into sequences, cluster them by similarity, and check whether the user is on track - or drifting from a successful path.
  
  

• Error interception
  
  Repeated form corrections or console errors trigger smart, in-context support - no need for the user to explain anything.
  
  

As we go, we’ll layer in lightweight machine learning:

• Matching sessions to past ones that worked, and suggesting what helped.
  
  

• Building models to predict when someone’s likely to drop off.
  
  

• Summarizing what’s happened in the session so far to guide next steps.
  
  

But we’re keeping it pragmatic.

Start simple. Respond early. Make chat feel like a product that’s actually paying attention.

Because it should be.