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Case Studies

Our goal is to maintain a personal approach to every Partner. We inject fuel into your business, so you can expect record-speed growth.

A proven track record across IT, Digital, and Tech

Focus on your core business while we handle all the routine work

A flexible team that plugs straight into your processes

Case Studies

Philips Speech Solutions

Philips Speech Solutions

We scaled Philips Speech Solutions with pre-vetted Full-Stack and .NET engineers, matched on tech stack, seniority and time zone. A fully operational remote team was assembled in under 7 days, with zero downtime in product development and maintenance.

Industry

Speech recognition & voice tech

Hq

Vienna, Austria

Solution

Remote engineering team

Type

Staff augmentation

Achievements:

Fully operational remote team in under 7 days

Zero downtime in development and maintenance

Pre-vetted Full-Stack and .NET engineers

Matched on tech stack, seniority and time zone

Factiverse

Factiverse

We recruited and placed a senior Python & ML engineer — strong in cloud, MLOps and AI product development — to support Factiverse’s real-time fact-verification and media-monitoring platform. The engineer integrated rapidly and grew into a long-term member of the AI team.

Industry

AI verification & media intel

Hq

Oslo, Norway

Solution

Senior Python & ML engineer

Type

Team extension

Achievements:

Senior Python & ML engineer, precisely matched

Cloud, ML and MLOps expertise

Rapid integration into startup workflows

Long-term engagement on the AI platform team

Data platform team (under NDA)

Data platform team (under NDA)

A scale-up in a regulated industry engaged us to stabilize a fragmented data stack. We embedded senior data engineers who rebuilt unreliable pipelines and introduced data-quality checks, giving the team data they could finally trust for reporting and AI.

Industry

Under NDA

Hq

Switzerland

Solution

Embedded data engineering team

Type

Team extension

Achievements:

Rebuilt unreliable data pipelines

Introduced automated data-quality checks

Trustworthy data for reporting and AI

Long-term embedded engagement

Client Engagement Roadmap

Before contract

Consultation with a fast audit of your data and stack

Free data audit, questionnaires

1st Day

Personal Customer Success Manager

Kick-off meeting

Engagement strategy based on SCRUM

Two weeks

Fixing data quality and reliability

Workflow setup and updated scope

In-depth audit of your data, tools and workflow

One month

Your data workflow is up and running

Regular, measurable deliverables

Integration into the Client’s workflow

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