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Australian fintech — embedded engineering scale-out

How we embedded a senior engineering team that shipped against a tight regulatory deadline — and grew from 3 to 8 engineers over the year that followed.

Client
AU fintech (anonymized)
Industry
Financial services
Published
April 20, 2026

The challenge

A Series-B Australian fintech faced a regulatory deadline that required net-new compliance features inside six months. Their in-house engineering team was strong but small — adding senior capacity through traditional hiring was going to take longer than the deadline allowed.

They didn't want a project shop. They wanted teammates who could meet a regulator's bar — and stay long enough to own what they shipped.

What we did

We embedded three senior engineers from our pipeline within ten days of the kickoff call. They onboarded into the client's tools, repos, and ceremonies — operating as part of the same engineering org, not as a separate vendor.

The partnership cadence was deliberately simple:

  • Specs upfront. Acceptance criteria for every workstream were defined and signed off before code was written.
  • Milestone-based delivery. Sprint scope tied to delivered features, not hours billed. One blanket payment hold was never on the table.
  • AI-augmented workflows. Every embedded engineer was already using modern AI dev tools in their daily work — review velocity and PR quality reflected it.
  • One escalation path. A single weekly leadership checkpoint between our delivery lead and their VP Engineering. Surprises surfaced early.

What shipped

MetricOutcome
Compliance feature delivery3 weeks ahead of regulatory deadline
Internal team time on net-new product+~40% vs. pre-engagement baseline
Embedded team size after 12 months3 → 8 senior engineers
Engineer retention across the engagement100% — no replacements needed

After year one

The engagement has expanded twice — first to scale the compliance team after the deadline shipped, then to take on a second product surface entirely. Same senior engineers, accumulating context over time.

That's the model: trust compounds when the same people stay. Velocity follows.

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