Engineers ready to start
A sample of our data engineers — anonymized. Full CVs available on request.
Case Studies
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
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)
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
Contact Us
