GoGloby

GoGloby was engaged by a publicly listed mobile gaming company with over 200 million players worldwide to build and embed a dedicated expansion team directly inside their organizational structure. The engagement was not a traditional hiring project: GoGloby's placed professionals operated as fully integrated members of the client's own team, leading the strategic regional expansion from within. The talent profile required combined gaming industry expertise with Applied AI capabilities — specifically the ability to leverage AI tools to operate at the scale and velocity that a small regional team otherwise could not sustain. This case study is relevant for any gaming company, entertainment studio, or technology firm that needs to expand into a new market fast using an embedded, fully integrated team rather than a separate outsourced function.

GoGloby placed specialized Applied AI Engineers directly embedded into the Data Science team of a top Ivy League university to help build the next generation of AI-powered educational experiences. The initiative required production-grade applied AI engineering depth — Advanced RAG, GraphRAG, multi-agent architectures on AWS, and generative AI models — under strict FERPA educational data privacy compliance. This case study is relevant for any university, research institution, or EdTech organization building production AI systems for learning environments, and for any company hiring AI engineers for education-focused or compliance-sensitive AI applications.

When a Nasdaq-listed HealthTech company acquired a leading cloud EHR platform serving 17 million patients and processing $3 billion in medical claims annually — the integration clock started immediately. The engineering challenge was real: multiple teams, multiple technical disciplines, a hard board deadline, and a platform where every bug has real consequences for real patients. Standard US hiring timelines would have missed the deadline by months. GoGloby placed 25 senior engineers across DevOps, Python/Data, Fullstack, and iOS in 58 days. Twelve months later, 96% of the team was still in place — now building the AI-powered features that define the platform's next chapter. 

A PE-backed vertical SaaS company — $11M ARR, 22 engineers, Series B — had purchased Copilot Business licenses for its entire engineering team. Months in, active daily usage hovered around 28%. The tools were installed. The workflows had not changed. The VP of Engineering could not demonstrate to the board what the AI investment had produced. GoGloby was brought in starting with a single embedded Applied AI Engineering Lead and expanding to a full applied AI team integrated into the core engineering organization, GoGloby's 4-layer program produced a 2.4× improvement in sprint throughput, a 37% reduction in PR cycle time, and the first board-ready engineering performance dashboard the company had ever run. The engagement is ongoing.

A PE-backed industrial ERP company — the #1 platform in its sector, serving 400+ enterprise clients in a $323B global market — was under board pressure to transform engineering performance through Applied AI. The CTO faced a problem that is increasingly common across PE portfolio companies: the mandate to deliver 2–5× engineering productivity existed at the board level, but resistance existed at every layer below it — internal legacy engineers, an entrenched European outsourcing vendor, and no internal leader to close the gap. GoGloby solved it with a structured Applied AI Engineering program: place the right leader first, build the AI-capable team around them, instrument the performance for board visibility, and operate in a private AI environment that protected the company's sensitive enterprise data. The result: a 10-person legacy team replaced by 5 Applied AI Engineers delivering 3.6× average performance — with real-time dashboards proving the outcome to investors in every board meeting.