Case Studies

Outcomes we can put numbers on

No vanity metrics. Every case study below includes the challenge, exactly what we did, and the measurable results our clients verified.

Healthcare software platform concept with glowing medical cross and data network

Orbit Health · HealthTech · Series B · 180 employees

Scaling a HIPAA-compliant engineering team from 8 to 22 in one quarter

The Challenge

Orbit Health had just closed a Series B and needed to triple its backend capacity to launch a telehealth product in two new states — but compliance-heavy hiring requirements had stalled their internal recruiting for four months.

Our Solution

We ran a dedicated search for Python/FastAPI engineers with prior HIPAA and SOC 2 experience, adding enhanced compliance screening to our standard 4-stage vetting. Seven engineers were placed across nine weeks, including a staff engineer who led the compliance architecture.

Measurable Outcomes

7 engineers

placed in 9 weeks

11 days

average time-to-offer

100%

12-month retention

“The compliance-aware screening saved us months. Every engineer arrived already fluent in HIPAA constraints — that almost never happens.”

Priya Raghavan — Head of Talent, Orbit Health

Global logistics concept with glowing world map and connected shipping routes

ShipFast Logistics · Logistics Tech · Scale-up · 400 employees

Standing up a full mobile delivery pod to launch ahead of peak season

The Challenge

With peak shipping season four months away, ShipFast needed a complete mobile team — iOS, Android, backend, and QA — to launch a driver-facing app. Internal hiring had filled exactly one of six seats in three months.

Our Solution

We built a dedicated pod of five engineers plus a delivery lead in three weeks, mapped to ShipFast’s sprint cadence and tooling. The pod shipped the MVP in ten weeks and stayed on to maintain it through peak season.

Measurable Outcomes

6-person pod

assembled in 3 weeks

3 months

launch ahead of schedule

99.2%

app uptime through peak

“The pod operated like an in-house team from day one — same standups, same velocity expectations. We launched three months early.”

Marcus Chen — CTO, ShipFast Logistics

AI machine learning concept with glowing neural network and data streams

Vertex AI Labs · AI / ML · Seed–Series A · 45 employees

Finding scarce LLM talent in the most competitive market in tech

The Challenge

Vertex needed three production-grade ML engineers with real LLM and RAG experience — a profile with fewer than a few thousand credible candidates globally and offer cycles routinely exceeding two months.

Our Solution

We tapped our private AI talent network, ran technical deep-dives with our in-house ML screeners, and coordinated a compressed two-week interview loop. All three offers were accepted, including one candidate who had declined two FAANG processes.

Measurable Outcomes

3 ML engineers

hired in 5 weeks

14 days

from first call to offers

3 of 3

offers accepted

“They found candidates we couldn’t even source, and their technical screening meant our founders only interviewed people who were genuinely senior.”

Elena Vasquez — Co-founder & CEO, Vertex AI Labs

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