Chris Silvestri is the Founder at Conversion Alchemy, where he helps B2B SaaS teams engineer message–market fit across web, sales, and email. Rising to prominence in the early 2020s, he became known for combining deep customer research, UX thinking, and decision-making psychology into scalable messaging systems that lift conversions rather than isolated campaigns. His work positions him as a widely regarded specialist for post–Series A SaaS companies seeking clarity, differentiation, and measurable revenue impact.
Previously, as Founder & Conversion Copywriter at Conversion Alchemy, he led projects that generated up to 30% more qualified demo requests by clarifying value propositions and sharpening differentiation on 20+ core website pages and sales assets. He became known for shortening sales cycles by an estimated 15–20% by making value obvious earlier in the buyer journey and aligning messaging with actual customer priorities. His systems consistently drove 10–15% lifts in trial-to-paid conversions while improving internal alignment across marketing, sales, and leadership.
His career highlights include serving as Conversion Rate Optimizer and UX Designer at Zeda Labs LLC from 2018 to 2021, where he blended qualitative research and experimentation to improve funnel performance and user experience over 2.5+ years. Earlier, he spent nearly a decade in engineering and industrial automation, experience that shaped his systematic approach to messaging, process design, and experimentation. Since 2020 he has also contributed to Good Product Club, writing on product strategy, UX, and go-to-market for teams building in an AI-driven world.
As host of the Message-Market Fit Podcast, he helps B2B SaaS leaders understand how to translate customer insight into narratives that win deals and defend pricing power. Through his Unpacking Meaning newsletter, he publishes weekly breakdowns of SaaS messaging, UX, and buyer psychology for an audience of founders, CMOs, and growth leaders.
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What a software engineer turned copywriter learned about positioning—and why 70% of the work happens before you write a single word.
“If you don’t have a process, AI is gonna produce crap,” Chris tells me. “If you have a process, AI is gonna produce good stuff.”
He says it like it’s obvious. Like the whole discourse around AI and creative work has been missing the point.
Chris Silvestri spent ten years as a software engineer in industrial automation in Italy before transitioning to copywriting. He moved to the UK, founded Conversion Alchemy, and now helps B2B SaaS companies find message-market fit. He writes for Every. He’s not worried about being replaced by AI. But he has thoughts about who should be.
I ask him to break down what he means by process.
“First do the research,” he says. “Then don’t feed all the research to AI and have it write—or sometimes they don’t even feed the research and just ask it to write, which is even worse.”
He pauses to let that land.
“Use the research, distill it into your strategy, and then use the strategy as context for the LLM. So they can actually make sense of the data better.”
This is the part most people skip. They dump raw transcripts and survey results into ChatGPT and expect positioning to emerge. But the synthesis—the actual thinking about what the research means—that’s human work. The AI can help you write after you’ve decided what to say.
“Seventy percent of the work to me is research,” Chris says. “And then the messaging and the copy almost write itself.”
I stop him. I want to make sure I understand the claim. He’s saying the writing is almost incidental?
He nods. The hard part is everything that comes before.
Chris’s engineering background shows up here. He sees messaging as a system with distinct layers. Positioning defines who you are. Messaging is how you articulate that across contexts—sales calls, landing pages, email sequences. Copy is the final layer, the actual words. Most people try to fix copy when the real problem is upstream. No amount of AI-generated headlines will save you if nobody agreed on what you’re saying in the first place.
“A lot of times different departments don’t really agree on what they do better or differently,” he says. “And so then everyone starts kind of saying different things.”
The jargon-stuffed copy that plague B2B websites? That’s not a writing problem. It’s an alignment problem.
I ask about how he approaches customer research when the data is thin. Early-stage companies often don’t have enough customers to build detailed personas.
“I think it’s useful to start with an archetype of your customers,” he says, “rather than saying, okay, this is a specific persona.”
He explains the distinction. An archetype is a representative of a group—business buyer versus technical buyer. Under the business buyer archetype, you might eventually differentiate between CMO, CFO, and procurement. Under technical buyer: CTO, data engineers, developers. But if you’re early, you don’t have the data to specify that precisely yet.
“We weren’t clear,” he says, describing a recent project with a data integration company. “So instead of crafting these ideal customer personas, we drafted these early customer personas. Business side, technical side. And from there we could move forward and get more specific.”
Personas come later, when you have crystal-clear data on psychographics, demographics, decision-making patterns. Archetypes let you start building without pretending to know more than you do.
This matters for AI workflows too. If you’re prompting an LLM to write for a persona you’ve fabricated from guesswork, the output will feel hollow. But if you’ve done the research—if you’ve actually talked to customers and heard how they describe their problems—you can give the AI context it can work with.
“The more you compartmentalize your tasks in LLMs, the better it works,” Chris says. “I don’t even use ChatGPT or Claude for writing directly. There are loads of third-party tools that let you plug into the APIs without that pre-training those commercial interfaces have.”
He’s building his own stack. One tool for finding signal. Another for working through strategy. A third for writing with his editorial style guide. Each chat stays focused. The synthesis happens in his head, not in the model.
Near the end of our conversation, I ask what led him to embrace AI when so many writers are defensive about it.
“I think first it was actually feelings of never being good enough,” he says. Something shifts in his voice. “Maybe it stems from the fact that I’m a non-native English writer. I’ve always said, what if I could be better? And then I saw AI, and now the playing field is level for anyone.”
He decided to try every tool he could find. Learn what actually works. Keep up with the changes happening every week. But what he discovered surprised him.
“Once you have a very specific and systematic process, AI can only amplify that.”
The people most equipped to leverage AI are the ones who invested in their own brains before these tools existed. They have vocabulary. They have frameworks. They know what good looks like.
Chris writes for Every now. He mentions how working with their editors makes him see things from a different perspective. The writer has one job. The editor has another. You try to mirror that same workflow when working with AI.
“The craft, the taste,” he says. “That just makes you better and amplifies your ability to do more with AI.”
I’ve been thinking about this since we hung up. The fear around AI in creative work is often misplaced. The tools don’t threaten people with strong processes—they expose people without them.
Seventy percent is research. The rest is finding the right combination of insights, framing, and context. If you’ve done that work, AI is just another tool in the kit.
If you haven’t, it’s a mirror.
