AWS Manufacturing Germany

Cambrium

Sustainability is becoming increasingly critical for businesses across industries, from manufacturing to consumer goods. As organizations look to reduce their environmental impact while maintaining or improving performance, they face the challenge of finding sustainable alternative materials.

2 hours
20 times
20x
23 percent

Reported impact

Business outcome

With these capabilities, Cambrium can develop sustainable materials 5 times faster and 20 times more cost-effectively than with traditional industry methods. The startup has used its pipeline to develop NovaColl, a sustainable collagen created through precision fermentation rather than animal products.

  • Cost Reduction
  • Productivity Improvement

Solution

Sustainability is becoming increasingly critical for businesses across industries, from manufacturing to consumer goods. As organizations look to reduce their environmental impact while maintaining or improving performance, they face the challenge of finding sustainable alternative materials. In just one month, a single Cambrium team member can build an entirely new protein analysis pipeline on AWS. When new protein files are uploaded to Amazon Simple Storage Service (Amazon S3), an object storage service, the pipeline is automatically triggered. This initiates a job running on AWS Batch, a fully managed batch computing service, to prepare and clean the data. These prepared files are then processed using NVIDIA-accelerated containers, which transform the proteins into vectors that are stored for future analysis.

Data flow

Data is stored in Amazon S3 and processed by Amazon SageMaker, AWS Batch.

Solution architecture

3 components · 2 layers
  1. Compute
    • Amazon SageMaker Supports model training, tuning, deployment, or managed ML inference.
    • AWS Batch Runs batch preprocessing and large-scale compute jobs for analytics pipelines.
  2. Storage
    • Amazon S3 Stores documents, media, telemetry, or training data durably at scale.

Architecture clues

  • In early 2024, the startup migrated its protein vectorization engine to AWS to scale its protein design pipeline.
  • These prepared files are then processed using NVIDIA-accelerated containers, which transform the proteins into vectors that are stored for future analysis.
  • Using BioNeMo–provided ESM models makes it possible for Cambrium to seamlessly integrate optimized protein vectorization endpoints into the stack.

Evidence from the source

As organizations look to reduce their environmental impact while maintaining or improving performance, they face the challenge of finding sustainable alternative materials.
Founded in 2020, Cambrium set out to transform the materials industry.
In just one month, a single Cambrium team member can build an entirely new protein analysis pipeline on AWS.
Sustainability is becoming increasingly critical for businesses across industries, from manufacturing to consumer goods.
When new protein files are uploaded to Amazon Simple Storage Service (Amazon S3), an object storage service, the pipeline is automatically triggered.
“Materials are everywhere around us,” says Pierre Salvy, chief technology officer of Cambrium.
Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Cambrium customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/case-study-cambrium/
    Source type
    Vendor-published customer story
    Published
    Jan 9, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.