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🏄 Riding the AI wave

🏄 Riding the AI wave

How Jetify Successfully Pivoted During the GenAI Boom

Rahul Berry & Aryaman Banerjee
March 18, 2025

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Hey everyone - as you’re all aware we are well and truly in the middle of the AI wave.

The market is flooded with agents and "AI-powered" nonsense, it's becoming increasingly difficult to separate signal from noise.

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That's why this week's conversation with Daniel Loreto, CEO of Jetify, stood out to us.

Unlike the countless founders who hastily slapped "AI" onto their product, Daniel approached the AI wave by answering two very important questions (that are much harder to solve than you may think):

Where does AI fit into the core problem Jetify is solving? (Focus of this edition)
Which customers should Jetify serve? (More on this next week!)

We guess Jetify answered both those questions pretty well given they recently closed USD 10Mn in funding from Google Ventures and Coatue to build out Test Pilot - an AI Quality Assurance (QA) engineer that's streamlining how companies leverage AI for QA work.

Let’s get into it!

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Some context…

When OpenAI kicked off the GenAI boom, Daniel and his team were already building developer tools to streamline software engineering environments. Their initial focus was on making it easier for engineers to set up consistent development environments - a solid business tackling a real problem.

As AI conversation intensified across the industry, Daniel confronted the question every founder was asking: Is this just hype, or a genuine transformative wave?

"I don't think it's absent of hype. I do think there are places where people are just adding AI to add AI. So we tried to ask ourselves: there are use cases for AI that create real value, and then there are use cases where AI is maybe just an adjacent feature. Can we actually create value?" – Daniel on assessing AI hype

Daniel knew this decision carried significant weight. Missing a technological wave of this magnitude could put Jetify's future at risk.

"AI is like a wave that only happens once in a while. The internet was a big wave, mobile was a big wave, and AI is another big wave. It doesn't happen every year, it may not even happen every 10 years. Jetify couldn't miss this wave." – Daniel on the importance of timing

After deciding to embrace AI, Daniel began by evaluating where AI fits into Jetify’s problem statement.

Daniel needed a systematic approach to find Jetify's unique position in an increasingly crowded market.

He outlined a three-step framework that any founders can apply when identifying opportunities in SaaS (not just AI)

Step 1: Map out present (and future) product verticals

What brainstorming a new AI idea looks like

Instead of chasing the hype, Daniel's team methodically evaluated where AI could create genuine value in software development:

"We were asking ourselves - for the problems that we're interested in solving, is there an application of AI that we feel would create real value? We were bouncing different ideas off and just being like, is that strong enough? Does that create enough value? Is that more just bolting AI on for AI's sake? It’s really important to be comfortable with ambiguity when deciding what you want to do"

Jetify was able to leverage their existing expertise to identify 5 opportunity areas:

Quality Assurance: Testing applications for functional bugs
Security Testing: Scanning for vulnerabilities and potential exploits
Internationalization: Adapting software for different languages and regions
Documentation: Creating and maintaining technical documentation
Code Review: Analyzing code for performance issues and best practices

Takeaway for founders: There’s no perfect formula for navigating new problem spaces. Don't be afraid to explore the unknown – sometimes the best approach is to identify multiple potential opportunities and test them systematically.

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Once they had their 5 opportunity areas, Daniel needed to narrow down by focusing on two criteria:

High value tasks i.e., the work is necessary but tedious
Low-satisfaction tasks i.e., developers would gladly offload them to an agent

"What are those parts of the software development process that are repetitive? Or maybe that are not the type of work that the core development team wants to be taking on that could be handled by AI?"

For Jetify, QA testing fit this profile perfectly. It's work that:

Must be done - Skipping QA leads to bugs, unhappy users, and technical debt
Consumes significant resources - Either developer time or specialized QA staff
Is largely repetitive - Following test scripts, checking functionality across platforms
Isn't core to product innovation - It's maintenance rather than creation

This is something that’s becoming a bit of a pattern in our interviews as Anish Dharr, CEO of Cortex, used the same criteria when he was originally founding his startup! (More on that here if you’re interested)

Takeaway for founders: The most compelling businesses target tasks that are both necessary and tedious. Look for work that's required for business success yet consuming significant resources - particularly processes that are largely repetitive or rule-based without being a source of competitive advantage or creative fulfillment for your users. Everyone wants to stop doing this type of work (and maybe AI can solve that).

Step 3: Think inside the box

As QA began to look like the right solution space, Daniel took a critical step that many founders overlook: he assessed whether his team had the expertise to actually deliver on each potential solution

"We ended up with QA because we had less in-house security expertise. If we wanted to go after the security angle, we'd need hardcore security engineers on the team, whereas with QA we were a lot more familiar with that process."

This was a pretty honest take from Daniel as it was likely a crucial filter in Jetify’s decision-making process.They recognized that while security testing might be an equally valuable application, the expertise gap would create significant challenges:

Longer time to market - They'd need to recruit specialized talent first
Higher burn - Specialized security experts command premium salaries
Execution risk - Without domain experts, they'd lack the nuanced understanding needed to build a superior product

By contrast, their existing expertise in QA processes meant they could:

Leverage institutional knowledge - The team already understood the problem space deeply
Move faster - They could start building immediately rather than staffing up first
Build with credibility - Their firsthand experience with QA challenges informed product decisions

Takeaway for founders: The most promising opportunity isn't always the largest market - it's the one where your team's existing capabilities give you an unfair advantage. Conduct an honest assessment of your current expertise and build where you have a head start.

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What this means for you

If you’re looking to integrate AI into your offering, Daniel teaches us a valuable lesson about not straying too far from what the initial purpose of your business was. Many founders get swayed with “potential” and “possibilities” of AI, but Daniel’s focus on leveraging what they had already built and powering that with AI is what caused the difference.

For Jetify, that was saving engineers time doing tedious, repetable (and frankly boring) QA work, what is it for you?