Introduction

When Paul joined Telescope, he was stepping into a rare opportunity: a blank slate. No legacy systems, no broken processes. Just a chance to build a go-to-market engine from scratch.

What he didn’t have was a RevOps team or the bandwidth to spend months wiring together tools.

In this conversation with Juliana from Evergrowth, Paul shares how he used Evergrowth to architect a lean, high-performing sales engine, capture better signals, and scale personalized outreach. All without writing a single line of code.

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Can you tell us about Telescope’s go-to-market strategy? (00:00:08)

Paul:
We're using technology to innovate in the staffing space. Existing solutions aren’t very engineer-friendly. They’re built mainly for the companies hiring, but even then, the stick rate of engineers isn’t great, so it’s a suboptimal system.

We’re trying to change that by finding better signals that show engineer quality and cultural fit with the hiring company.

I joined Telescope earlier this year to help the team transition from inbound, founder-led sales to scaled outbound sales.

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What did your tech stack look like before Evergrowth? (00:00:55)

Paul:
One of the main reasons I took this role was that I could start with a blank slate. I didn’t have to dismantle a broken tech stack — I got to build one from zero.

The first few months, I focused on architecting. I wanted to identify the signals and data I needed to go to market effectively — to sound more human and personalized in outreach, but still do it at scale without it becoming too time-consuming.

My stack now is simple but powerful: Evergrowth → HubSpot → DAI. I use Evergrowth for capturing and enriching signals, HubSpot for sequencing, and DAI for pipeline management.

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What made Evergrowth stand out compared to other tools? (00:01:56)

Paul:
If you look at the market, there’s a spectrum. On one end, you’ve got out-of-the-box tools that do everything for you — almost like services rather than products. On the other, you’ve got highly technical tools that let you build your own bespoke setup.

I found Clay really intuitive and easy to learn, but hard to master. I’m not very technical. At one point, I thought I might be a low-code type of person, but really, I’m more no-code. I don’t have a RevOps team or the budget to hire GTM engineers internally or externally.

The out-of-the-box tools were too restrictive. You couldn’t see how agents were prompted, or add new signal types — you were stuck with whatever came pre-built.

Evergrowth is the perfect middle ground. I can spin up new agents, test and refine signals, and deactivate the ones that stop being useful. It’s easy as a non-technical user to build dozens of agents and orchestrate them effectively.

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What was your first impression when you saw Evergrowth? (00:03:42)

Paul:
The two things that stood out were flexibility and orchestration. I loved that I could create my own agents and run mini-evals — basically a sandbox where I could tweak prompts until I got consistent, accurate outputs.

Before Evergrowth, orchestration was the hardest part. I could use ChatGPT, Claude, Clay, and other tools, but tying them together with Zapier or Make just wasn’t scalable. It felt fragile. Evergrowth solved that immediately.

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How was the onboarding experience? (00:04:40)

Paul:
Onboarding was a real highlight. With tools that give you so much control, the learning curve can be steep. But the Evergrowth team went above and beyond to help me ramp up quickly and use the platform effectively.

I feel pretty proficient now, but those first couple of months were great for learning the ropes and improving my prompting. The team also shared broader best practices — not just how to use Evergrowth, but how to think about prompting and agent orchestration in general.

Were there any “aha” moments during onboarding? (00:05:31)

Paul:
For better or worse, I’m the only user right now, but that made it easier to get hands-on. The big “aha” moment came even before purchase — during JB’s demo. Everything he showed me about the product actually held up in reality. That’s rare.

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What campaigns are you running right now? (00:06:07)

Paul:
I ran a few beta campaigns to test the orchestration and get things moving. My first real campaign launched earlier this month.

Right now, I’m targeting portfolio companies of venture firms that have invested in my existing customers. It’s been awesome so far — Evergrowth has helped me deeply enrich those accounts so I can send outreach that’s both personalized and scalable.

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What outcomes are you measuring from your Evergrowth project? (00:06:48)

Paul:
The main goal is meetings booked. We’re still early, but response and open rates are way above benchmark.

I don’t have a “before Evergrowth” comparison here, but I’ve spent years in martech, so I know what good looks like. We’re seeing open rates three to five times higher than standard, which means the personalized signal capture and outreach are working.

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How does Evergrowth compare to other tools you’ve used, like Clay or Apollo? (00:07:43)

Paul:
Honestly, it replaces most of them. I don’t use Apollo or Clay anymore. Evergrowth doesn’t try to be a sequencer — and that’s a good thing. You can’t be everything to everyone. It’s better to do one thing exceptionally well, which Evergrowth does.

The HubSpot integration is seamless. I get enrichment, build my account overviews and outreach plays, then sync those into HubSpot for execution. It’s a lean, extensible stack.

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What other initiatives are you integrating into your GTM stack? (00:08:27)

Paul:
I’m starting to bring in warm-path data. The idea is to combine warm introductions with cold outreach so they complement each other.

If I want to reach someone, I’ll find a warm path for an intro — but I’ll also have a cold campaign running so they’ve already seen my name or messaging. When the intro happens, they’re primed, and it works beautifully.

How has Evergrowth changed your workflow? (00:09:44)

Paul:
There’s no before-and-after story since I built everything from scratch, but Evergrowth is at the core of my workflow. It fits naturally, automates what needs to be automated, and saves me hours every week.

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How does this compare to previous teams you’ve built? (00:10:23)

Paul:
In past roles, we used multiple tools to capture signals, but they didn’t talk to each other. I had to manually combine the data, and the signals were often too generic to be useful.

That’s not the case with Evergrowth. The orchestration layer brings everything together seamlessly, and the signals it captures are specific, high-fidelity, and actionable.

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What advice would you give to GTM or RevOps leaders evaluating tools? (00:11:46)

Paul:
Stop and think before you buy. I see a lot of teams with tool bloat — six or more tools that don’t integrate well. It creates headaches.

Start with your strategy. Define the data points you need for success, then find tools that capture that data efficiently. Evergrowth does that for me.

If I’d just bought the first flashy tool I saw, I’d be in a much tougher spot now.

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How do you see your GTM model evolving in the next 12 months? (00:13:13)

Paul:
I think we’ll see less AI spamming and more thoughtful, human-like outreach powered by AI. For me, it’s about continuing to iterate — refining agents, deprecating signals that don’t work, and building new ones that do.