Building Klöckner Pentaplast's global talent plan with Claude

~5700

Employees

$2.2B

Revenue

Plastics Manufacturing

Industry

Luxembourg

Headquarters

The Core Challenges

No single view of talent data
Before working with Draup, KP did not have a reliable way to see talent data across its work sites.
Inconsistent role definitions
Role definitions varied from place to place.
Scattered hiring and compensation data
Information on hiring, availability and compensation sat in different teams and countries, which made planning slow.
Thin data in smaller markets
It was especially hard to get a clear picture in smaller manufacturing markets where public talent data is thin.
OUTCOME
With Claude, Draup helped KP see where to find specialist talent and where to build teams
So they could plan their global workforce faster and back it with solid data.

What we did

Draup used its talent intelligence platform to find specialist talent, compare locations, and benchmark peers for KP, with Claude Cowork and Claude Code supporting the work throughout.

KP wanted better data behind its hiring and workforce planning. Their talent acquisition team was finding it hard to get clear, usable information on niche roles across more than 15 global locations, which made it difficult to plan and to make confident decisions. Draup used its talent intelligence platform to help them find specialist talent in sustainable packaging and films, compare locations on talent availability, cost and growth, and see how peer companies were sourcing and building similar skills.

Draup's team used Claude, through Claude Cowork and Claude Code, alongside the platform. Claude helped check and clean the platform outputs, organize the job role and skills taxonomy, move faster through the talent, location and benchmarking analysis, pull out the key insights, and put together the final readouts and storylines shared with KP.

Claude in the workflow

Draup's platform produced the talent, location and peer intelligence. Claude Cowork and Claude Code helped Draup's team check it, structure it, and turn it into readouts KP's leadership could act on.

Claude Cowork & Claude Code
Platform outputs, checked and cleaned
Claude helped check and clean the outputs from Draup's talent intelligence platform, so the numbers behind the talent, location and benchmarking analysis held up.
Numbers checked before the readout
Claude Cowork & Claude Code
Role and skills taxonomy, organized
Helped organize KP's job role and skills taxonomy, the structure behind the flexible, skills-first framework Draup built for the team to keep using.
Skills-first framework
Claude Cowork & Claude Code
Analysis to leadership readouts, faster
Moved faster through the talent, location and benchmarking analysis, pulled out the key insights, and put together the final readouts and storylines shared with KP.
Readouts and storylines for leadership

Outcomes

A single, clear view of talent

KP moved from scattered talent data to a single, clear view it could act on.

Insight in markets where data is thin

Draup gave the team solid insight even in smaller markets where data is usually hard to find, which strengthened both hiring and location planning across a business of roughly 5,700 employees and around $2.2 billion in revenue.

Peer benchmarks and a skills-first framework

Peer benchmarks improved how KP positioned itself for talent, and the skills-first framework was built to hold up as its needs change.

A structured view of competitor leadership moves

A structured way to track competitor leadership moves now guides role design and succession.
The support from the Draup team has been second to none. Their comprehensive talent intelligence and strategic insights have been instrumental in our hiring and talent pipelining, enabling us to make informed decisions and strengthen our competitive edge.
Svenja Brandenburg
Former Talent Acquisition Director
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
SOC 2
GDPR
CCPA
ISO 27001
ISO 42001
EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, CCPA, ISO 27001, and ISO 42001. As an EAIGG member, we audit for bias and build trustworthy AI.

Data Trust &
Methodology
at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company.

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.

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