By Karthi Sepulohniam (Subramaniam), Head of Asia Pacific & Australia, Partners for Growth
Partners for Growth AI Series – Episode 4
Featuring Chris Hexton CEO & Co-Founder, Vero
The best AI lessons come from the people shipping it.
This is the fourth instalment of our AI Series, where we sit down with technology leaders across our ecosystem to understand how they’re using AI in ways that are practical, non-obvious, and genuinely changing how they operate.
For this episode, I sat down with Chris Hexton, CEO & Co-Founder at Vero. Vero is a customer engagement platform. Marketers at digital companies use it to automate the emails, SMS, and mobile push messages they send to drive engagement with their products. Founded in 2012, the platform now delivers over five billion messages annually.
The interesting thing about Vero is the team size: they compete directly with companies like Braze, which has roughly 2,000 employees, but Vero does it with a fraction of that. I sat down with Chris Hexton, Vero’s CEO and Co-Founder, to understand how AI is reshaping what a lean team can achieve and where the limits are.
The Small Team Advantage (For Now)
Chris’s view on AI adoption is that a small, senior team has a genuine window of advantage right now. When everyone on the team has autonomy and strong judgment, adopting new tools and putting them to work happens quickly. There’s no coordination overhead, no layers of approval, no need to train hundreds of people.
But he’s clear-eyed about the shelf life of that advantage. Larger competitors will eventually figure out how to disseminate these tools across their organisations. The window for small teams to move faster is a head start, rather than permanent advantage.
What Vero has done with that head start is significant. Engineering velocity has increased substantially, and the team is now working on closing the loop between shipping code and getting it in front of customers, automating changelog entries, social media updates, and product announcements so that a small team can look, and operate, much bigger than it is.
From Craftsman to Supervisor
One of the most candid moments in the conversation was Chris describing the cultural shift AI has forced on his engineering team.
The day-to-day work of an engineer has gone from being a craftsman of code to supervising a robot. That’s not inherently exciting. The thing that drew most engineers into the profession has, to a large degree, been abstracted away. Any engineering leader’s challenge is reorienting the team’s motivation around output and impact rather than the act of writing code itself.
Part of that has meant making sure people aren’t afraid they’re going to lose their jobs. The opportunity, as Chris sees it, is to do more for customers, not less. But that message only lands if the team can see it playing out in practice – shipping features faster, watching customers use them, and feeling the momentum that comes from higher velocity.
The second challenge has been knowledge sharing. When one team member figures out a powerful new workflow, how do you get that insight across the rest of the team quickly? With tools like Cursor changing dramatically week to week and new models releasing constantly, keeping everyone current is an ongoing problem that Chris admits they haven’t fully solved.
The Tool Stack: Fluid by Design
Vero doesn’t standardise on a single AI coding tool. The team uses Cursor, Claude Code, and OpenAI’s Codex interchangeably, and individual engineers have preferences that shift as new models and features roll out.
Rather than mandating one tool, Chris and his team have given everyone access to all three and let the team experiment. Different tasks suit different tools, and the pace of change in the space means what works best this month may not be the best option next month. It’s a deliberately fluid approach, and one that works only because the team is small and senior enough to evaluate quality on their own.
Beyond coding, the team uses Linear – an engineering task management tool – whose built-in agent has become a key part of their product marketing automation. When an engineer closes a ticket, Linear can automatically draft an announcement based on what changed in the code. It’s one piece of a broader effort to collapse the gap between shipping a feature and telling customers about it.
The MCP: Most Requested Feature in a Decade
The single AI-related product decision that’s had the biggest impact is Vero’s release of its own MCP – a Model Context Protocol interface that lets customers interact with the platform through AI assistants like ChatGPT rather than through Vero’s traditional UI.
It was, by Chris’s account, the most requested feature in at least five years, possibly a decade. The volume and consistency of demand was unlike anything the team had seen in a long time.
What’s playing out in practice is fascinating. Some customers now build their audience segments in Snowflake using a Snowflake MCP, then pass the list to Vero’s MCP to create and send the campaign – all within a single ChatGPT conversation. In these cases, some aspects of the workflow that used to happen inside Vero’s product are now happening outside of it.
That creates a tension Vero is watching closely. On one hand, it lowers switching costs as customers are less locked into Vero’s segmentation tools. On the other, it’s driving more engagement and more messages sent, which is how Vero prices. The net effect so far has been positive, but the long-term implications are still unfolding.
Reverse-Engineering How AI Recommends You
The most immediately actionable insight from the conversation was how Vero doubled its inbound demos in a single quarter by optimising for how large language models recommend products.
When someone asks ChatGPT “what’s the best customer engagement platform?”, it draws from a set of web sources, usually comparison articles and blog posts. ChatGPT even links to these sources, so you can see exactly what it’s citing.
Vero reverse-engineered those citations. They identified the types of articles being referenced, wrote similar content on their own blog, leveraging 14 years of domain authority, and made sure they were present in the Reddit and LinkedIn discussions that LLMs also draw from.
The results were measurable. Vero went from being mentioned in less than 5% of relevant ChatGPT responses to 12%, which had a meaningful impact on demo volume.
It’s a playbook that any company with a strong content foundation could replicate, and it’s a clear signal that optimising for AI recommendations is becoming as important as traditional search.
The Growth Repository
When I asked about non-obvious AI use cases, Chris described something deceptively simple. Vero has built a code repository, not for software, but for marketing.
The repository contains every blog post Chris has ever written, every marketing page, every email, every LinkedIn post, plus a folder of brand voice guidelines and style rules. When the team runs Claude Code against it, the AI can draft new content that draws on the full history and voice of the company. The team treats marketing content like a codebase – version-controlled, structured, and accessible to AI – and is a step many marketing teams are yet to take. It’s the difference between asking an AI to write in your voice and giving it enough context to actually do it.
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This is Episode 4 of AI: Where Innovation Meets Execution, a series where we profile how technology companies and investors across our ecosystem are deploying AI in practical, non-obvious ways. If you’re building something interesting with AI and want to be featured, reach out to us at karthi@pfgrowth.com.
Vero: getvero.com
Featured companies may be clients or portfolio companies of PFG. No compensation has been provided for inclusion. This content is for informational purposes only and does not constitute investment advice or a recommendation.
The views expressed are my own and do not necessarily reflect those of my employer.
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