Why We Rebuild AiSDR Workflows Every 6 to 9 Months

I had a realization recently that caught me off guard:

At AiSDR, we rebuild the core workflows of our product every six to nine months.

I’m not talking about incremental improvements, bug fixes, or UI tweaks. 

I mean foundational systems. The engines that power the product:

• Lead search and enrichment
• Onboarding flow
• Sequence building
• Persona building
• AI strategist

And these aren’t just iterated or optimized. We’ve torn them down and rebuilt them from scratch multiple times.

When I realized this, I was a bit shocked at first. But the more I thought about it, the more it made sense.

We’re not rebuilding because something’s broken. We’re rebuilding because AI is advancing so quickly that yesterday’s best solution is no longer good enough.

AI advancements don’t wait for your roadmap

If you’re building a product powered by AI, you’re not just competing against other startups. You’re competing against what’s newly possible, every few months.

And that bar keeps moving.

Take our lead search and enrichment engine as an example. We launched it, only to rebuild it twice. 

And that still didn’t stop us from throwing it out and starting from scratch once more nine months later.

That’s because new tools and AI reasoning models gave us the opportunity to make it dramatically better.

Even now, I already know what I need to change for the next version.

But that’s just the pace of this space. It’s relentless. And in this world, “working” isn’t the benchmark. “Working” becomes outdated the moment something more powerful is possible.

The only way to stay relevant in this space is to keep up with what’s possible, even if you’re tearing down your best work when it’s no longer the best approach.

Why we spend so much time building foundations

This isn’t about adding bells and whistles. It’s about rethinking the entire engine behind the product.

One month, we completely rebuilt our onboarding flow.

The next month, we rebuilt the outreach sequence engine.

And the month after that? We’re probably going to reimagine another foundational workflow.

Why? 

Because when new AI models shift what’s possible, workflows that felt modern three months ago start to feel clunky once you’ve seen what a new generation of models can do.

To stay ahead, we don’t patch over yesterday’s success. We build. Not reactively. But deliberately and repeatedly.

What it really takes to build AI products that win

Building an AI product isn’t like building traditional SaaS.

In SaaS, you iterate. You optimize. You plan and ship roadmaps.

In AI, you reinvent. You rethink the entire approach when the tech demands it. And this is the key mindset shift that more product teams need to embrace.

Here’s what it really takes if you want to turn cutting-edge AI capabilities into practical outcomes for your customers:

• Your product must be designed to switch between AI models fast
• Your engineering team must be willing to rethink core workflows often
• Your product roadmap must flex around AI capability shifts, and not just customer feature requests
• Your leadership team has to embrace that today’s success might need to be torn down tomorrow

This is hard to do, especially when the version you’re replacing already works. And it’s a very different cadence from traditional SaaS.

But it’s also what gives you the edge.

In AI, working isn’t enough. You have to ask: “Is this still the best way to do this, given what’s possible now?”

And if the answer is no, you rebuild.

Behind every AI success is the team that builds it

This kind of product development is exhausting if you’re not set up for it.

You have to be willing to let go of your best work. You have to be hungry to outdo yourself. You have to create space for uncertainty, and still ship fast.

That’s why I feel incredibly grateful to work with the team we have at AiSDR.

They don’t just adapt to change. They drive it. And they’re never afraid to scrap what’s familiar in favor of what’s better. And they bring the kind of speed, creativity, and resilience that’s required to build an AI-native product.

We’re not just building an AI product. We’re building the muscle to stay relevant and deliver value to users, even as the ground keeps shifting.