AWS Commercial and Professional Services Germany

AVANTGARDE Group

Real-time Analytics

AVANTGARDE wanted to offer companies a new way to elicit attendee feedback from off-line events without using postevent surveys, which tend to have low participation rates. “Building everything from scratch ourselves wouldn’t have been possible.” Because what we’re doing is at the cutting edge, we looked to AWS for support when we had challenges.

Making Events Measurable Founded in 1985 as an event agency, AVANTGARDE has developed into a globally active group of companies with more than 650 employees in 11 locations worldwide.

Reported impact

Business outcome

“Our solution is doing things in the physical world that could not be done until now,” says Sponholz. “We have found AWS to be super helpful by incorporating our feedback into the road map so that we could build the features that we needed.” Achieving a high level of security for communication between edge devices and the cloud was a top priority.

  • Customer Experience
  • Revenue Growth
  • Scalability

Solution

AVANTGARDE turned to Amazon Web Services (AWS) and used AWS for the Edge —which brings AWS cloud capabilities to organizations—and Internet of Things services on AWS to develop a solution that can be used to operate machine learning (ML) models using on-premises devices. AVANTGARDE can run its ML models using AWS for the Edge, which lets smart devices process data either at the local level using servers or at device level using ML algorithms. AVANTGARDE’s solution analyzes emotional expressions to interpret attendee perception and engagement, then converts this data into an experience score. The solution uses image sensors to capture attendee expressions, which are converted to data that customers can access through an analytics solution.

Data flow

Securing Communications between the Edge and the Cloud using AWS IoT Greengrass AVANTGARDE started developing its AI solution in 2021 and initially ran into challenges finding components for the devices—smart sensors similar to those used in autonomous vehicles—that it was building.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon SageMaker Neo Optimizes trained models for efficient inference on supported edge hardware.
    • Amazon SageMaker Supports training, fine-tuning, and managed inference for machine learning workloads.
    • Amazon SageMaker Edge Runs optimized machine learning models on edge devices close to data sources.
  2. Orchestration
    • AWS IoT Greengrass Deploys and manages software on industrial or edge devices close to operations.
  3. Governance
    • AWS Security Hub Aggregates and automates cloud security findings and best-practice checks.

Architecture clues

  • By using Amazon SageMaker Edge, which runs on almost every hardware solution, along with Amazon SageMaker Neo , a service for optimizing ML models for inference on Amazon SageMaker in the cloud and supported devices at the edge, the group could start building models even without yet having decided on a specific hardware component.
  • Securing Communications between the Edge and the Cloud using AWS IoT Greengrass AVANTGARDE started developing its AI solution in 2021 and initially ran into challenges finding components for the devices—smart sensors similar to those used in autonomous vehicles—that it was building.
  • “Because what we’re doing is at the cutting edge, we looked to AWS for support when we had challenges,” says Sponholz.
  • “We have found AWS to be super helpful by incorporating our feedback into the road map so that we could build the features that we needed.” Achieving a high level of security for communication between edge devices and the cloud was a top priority.

Evidence from the source

AVANTGARDE wanted to offer companies a new way to elicit attendee feedback from off-line events without using postevent surveys, which tend to have low participation rates.
AVANTGARDE’s solution analyzes emotional expressions to interpret attendee perception and engagement, then converts this data into an experience score.
Brands can use these scores to get more accurate insights on everything from event speakers to retail environments, then apply this information to optimize the experiences they offer—whether that’s by reimagining an entire retail environment or simply changing the music and lighting at an event.
In addition to the brand experience agency AVANTGARDE, the group has other specialized agency brands, such as Slash.Digital , Trendbüro, and Green Game, offering a broad spectrum of solutions and services.
Making Events Measurable Founded in 1985 as an event agency, AVANTGARDE has developed into a globally active group of companies with more than 650 employees in 11 locations worldwide.
Transparency Evidence & methodology 66/100 reference value · 85% 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 value66/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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
    5 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 specificity8/10
    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

    AVANTGARDE Group customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/avantgarde-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 1, 2023
    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.