How Vantix Hired Its First AI Engineer Without Sacrificing Quality For Cost

About Vantix

Vantix is a Series A fintech startup building in stealth. Their product is an AI-driven underwriting engine that could change how lenders assess risk on thin-file borrowers, people without a long credit history, but at the time they came to us, none of that was public. They'd raised their Series A eight months earlier and had 22 employees, with engineering split between a small core platform team and a product team racing toward their next funding milestone. Their roadmap had one blocker: a model-serving layer that needed an engineer who could take an AI model from notebook to production, not just someone who'd worked adjacent to AI tooling.

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The Problem

A few things were working against them at once:

• Being in stealth, confidentiality mattered more than usual. They weren't ready to have an outside engineer talking about what they were building before they'd even announced it.
• Their engineering lead was already stretched across platform work and didn't have bandwidth to closely manage a new hire.
• They needed someone who could own the AI components independently, not someone who needed hand-holding on fundamentals.
• They'd been burned before. A freelance engineer they'd hired the previous year had claimed AI experience in the interview, then spent two months producing code that didn't hold up in production, costing them both the engineer's fees and the six weeks it took to find and onboard a replacement.
• Because of that, their CTO was skeptical of hiring claims in general and wanted proof before committing to a full engagement, not another set of promises.
• They were also wary of the usual staffing options: a freelancer meant they'd be back to managing someone themselves, and the one offshore agency they'd spoken to wouldn't commit to anything if the hire didn't work out.

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The Solution

We went into our AI engineering talent pool and shortlisted three candidates within a week. Each candidate had already been through IKE, our five-point vetting system, before we presented them to Vantix, so the baseline competency question was already answered by the time Vantix saw a profile.

Given the stealth requirement, we put an NDA in place before any candidate saw product details, and briefed each shortlisted engineer only on what they needed to evaluate fit, not the full scope of what Vantix was building.

Vantix's CTO interviewed all three over two days and selected one engineer with a background in ML infrastructure.

What stood out to me was how much they actually held our hand through the process. They didn't just send us a few CVs and leave us to figure out the rest. They took the time to understand what we needed, helped us define what success looked like, stayed close throughout the hiring process, and made sure we weren't taking on all the risk ourselves. - Michael Tisdale

The real obstacle wasn't finding someone qualified. It was trust: how do you know, before you've paid for a month of work, that this person will actually deliver? We addressed that directly with outcome-based billing for the first month. No fee unless the engineer hit benchmarks both sides agreed on upfront.

Before the engineer started, we sat down with Vantix's engineering lead and mapped out what success needed to look like at 30, 60, and 90 days: specific deliverables tied to their actual roadmap (ship the model-serving pipeline, integrate it with their existing risk engine, get it stable under production load), not a vague sense of "things going well." That gave both sides a shared, concrete standard to check progress against, rather than a gut feeling three months in.

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The Results

• Month 1: the engineer shipped the model-serving pipeline Vantix had been stuck on for six weeks, hitting every benchmark agreed upfront. Our invoice was paid in full, no negotiation, no back-and-forth.
• Months 2-6: confident in the process, Vantix came back to hire three more engineers through us, one cloud engineer and two backend engineers, all working inside the same AI-enabled workflow established with the first hire, and all under the same confidentiality terms.
• Cost: across the four hires, Vantix estimated it saved roughly $120,000 against what the same team would have cost to hire locally in the US.
• Speed: their average time-to-hire dropped by more than half compared to their previous process, which had involved multiple recruiters and a six-week freelancer vetting cycle that didn't even work out.
• Outcome: the model-serving pipeline shipped on schedule, became the backbone of their underwriting product, and the team scaled without the engineering lead taking on any additional management load, or any confidentiality concerns surfacing.

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What's Next

With their engineering team in place, Vantix is now turning to the parts of the business that don't touch code directly. They've opened a search with us for their first product designer, to rebuild the customer-facing dashboard before their next funding round, and their head of growth has flagged a performance marketing hire for Q1 to support a planned push into a new lending vertical. Vantix's CTO has said he'd rather expand with us than restart the hiring process from scratch for every new role.