Case Studies
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
Engineers ready to start
A sample of our data engineers — anonymized. Full CVs available on request.
Not a fit? We replace the engineer.
If an engineer isn’t working out, we replace them within 2 weeks — at no extra cost.
- No upfront fees
- Pay monthly
- No lock-in — cancel anytime
More 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
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
15-min call: role, stack and time zone
Matching CVs to review and interview
1st Day
Personal Customer Success Manager
Kick-off meeting
Engagement strategy based on SCRUM
Two weeks
Onboarding into your tools and backlog
First deliverables shipped
Scope and priorities fine-tuned
One month
Your data workflow is up and running
Regular, measurable deliverables
Integration into the Client’s workflow
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