Last Tuesday, I watched a customer struggle for 3 hours to find an answer. The answer was already in their knowledge base, buried in paragraph 4 of a PDF uploaded eight months ago.
This happens thousands of times a day. Everywhere.
But that's not the story I want to tell you.
A few months ago, I had a conversation that changed everything.
We were on a call with one of our customers, an e-commerce company that's been using Quivr for almost a year. I asked them what they liked most about the product. I expected them to talk about the AI, the automatic responses, the first contact resolution rate.
Their answer caught me off guard.
"Honestly Stan, what we like most is you. Last week, you sent us a message saying you'd noticed a spike in return tickets. We hadn't even seen the problem. You saw it before we did."
I hung up. My co-founder and I talked about it for three days.
We Had It All Wrong
For months, we'd been focused on the product. How do we make the AI faster? How do we improve response quality? How do we automate more tickets?
We were optimizing the wrong thing.
Our most loyal customers, the ones who stayed, the ones who referred us, the ones who sent us thank-you messages, weren't the ones with the best features. They were the ones we worked with closely.
The ones we'd Slack to say "Hey, I looked at your data this morning, there's something weird with your shipping tickets since Tuesday."
The ones whose problems we'd dig into before they even knew the problem existed.
The ones we treated like partners, not users.
“Our secret sauce wasn't our AI. It was us. The humans behind it. The fact that we were proactive.
The Pattern We Discovered
Once we saw it, we saw it everywhere.
At Quivr, we went back through all our churns from the past six months. The customers who left. And we looked for the common thread.
It wasn't price. It wasn't missing features. It wasn't AI quality.
It was silence.
The customers who left were the ones we'd left alone. The ones we hadn't messaged in three weeks. The ones whose usage drop we hadn't noticed. The ones who had a problem and had to come to us to report it.
Sound familiar? It's the opposite of what a good customer effort score should look like.
On the flip side, our best customers all had the same experience: we came to them. We anticipated. We noticed things before they became problems.
One of our customers told me something that stuck with me:
"With you, it feels like I have a teammate watching my back. Not just a tool I use when I need it."
Teammate vs tool. That distinction became our obsession.
The Problem: It Doesn't Scale
There's an obvious catch with this approach.
We're a small team. We can be proactive with 50 customers. Maybe 100 if we're organized. But 1,000? 10,000? Impossible.
Proactive human support is the best customer service there is. But it's also the least scalable. You can't keep hiring people to monitor each customer's data and send them personalized messages.
That's when it clicked.
What if AI could do that?
Not the reactive AI we all know. Not the chatbot that sits there waiting for a question. Not the assistant that answers tickets.
An AI that does what we did. That looks at the data. That notices anomalies. That taps you on the shoulder and says "hey, I saw something weird."
What if you could give every customer the experience of having a dedicated team watching their back, without it costing a fortune?
What We're Building
At Quivr, we started imagining what this would actually look like. Not abstract features. Behaviors. Moments where the AI would come to you instead of waiting.
“You've edited my replies 12 times this week on international shipping questions. I think I'm missing context about your new EU policy. Want me to draft a knowledge article about it?”
Kelly doesn't just learn from her mistakes. She identifies why she's wrong and offers to fix the problem at the source. Like a colleague who comes to you and says "I don't think I have all the info on this topic, can you explain?"
“47 refund requests this week for orders from your Paris warehouse, all delayed by 3+ days. That's 3x your normal rate. Something's happening there.”
Michael doesn't just process refunds. He connects the dots. He sees the pattern you don't see because you're heads down in the day-to-day. He gives you the info you need before it becomes a crisis.
“Your biggest enterprise client hasn't logged in for 9 days. They usually check in daily. Want me to draft a check-in email?”
Joana does what we used to do manually: watch for weak signals. A VIP customer going quiet. A trial user stuck on onboarding. A sales opportunity going cold. Except she can do it for thousands of customers at once.
Why This Is Different
I know what you're thinking. "Another company promising revolutionary AI."
Fair enough.
Most support AI is built to respond. Their main metric is response time, auto-resolution rate, tickets processed. We've compared ourselves to the biggest players, and this is where everyone focuses.
These metrics optimize for reaction. They assume a ticket already exists. That the customer already had the problem. That they already took the time to write.
We want to optimize for something else: problems prevented.
Tickets that never needed to be written because someone anticipated. Customers who never churned because we saw the signals before it was too late. Crises that never happened because we connected the dots in time.
This isn't just intuition. The data backs it up. Companies that invest in proactive have radically different results than those that just optimize for response speed.
The Bet We're Making
Let's be honest: we haven't built it all yet. Kelly, Michael, Joana, they exist in our vision, in our specs, in our early prototypes. But we're not there yet.
What we have is a conviction.
The future of customer support isn't faster chatbots. It's not more accurate responses. It's not more automation.
It's AI that does what the best humans do: notice, anticipate, prevent.
“A tool waits for instructions. A teammate watches your back. We're building teammates.
We're making this bet because we've lived it ourselves. We know what it feels like to have someone proactive on your team. We know the difference it makes for customers. We know that's what creates loyalty, trust, referrals.
And we think we can put that in a product.
What You Can Do Today
You don't need to wait for us to start thinking proactively. Here's what we'd recommend this week:
- 1Audit your repeat questionsIf the same questions keep coming, your knowledge isn't reaching users. That's problem #1. Use your ticket data to find the top 10.
- 2Track time-to-answer, not just resolutionThe clock starts when the user has a problem, not when they open a ticket. How long do they struggle before reaching out?
- 3Build one proactive triggerPick your most common question. Set up an alert or workflow that answers it before it's asked. Start small.
Join Us
If this vision resonates with you, we're looking for teams to build this with us.
Not just beta testers. Partners. People who've felt the frustration of reactive support and want something different. Teams who are ready to tell us what works, what doesn't, what we missed.
Want to build this with us?
We're looking for early adopter teams to co-create the first proactive agents. Limited spots.
Join the adventureThe best customer service I ever received was a guy at a SaaS company who called me to say "I saw you struggled with this feature, want to hop on a call?"
I want us to be able to offer that to everyone.
That's our bet.
Thoughts? Questions? I'm at [email protected], and yes, I read every email.
P.S. If you want to see how we stack up against the current solutions, check out our comparison with Zendesk AI. Spoiler: we think differently.
