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Launchmetrics

Data Lake / Data Warehouse Modernization · Document Processing · Marketing Personalization

That’s the challenge that Launchmetrics , a marketing company driven by artificial intelligence (AI), helps fashion, lifestyle, and beauty brands resolve.

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It has sped up prototype development from as long as 5 months to just a few weeks while increasing the quality of its supervised learning tools.
It processes and stores up to 7 million documents a day in this data lake.

Reported impact

Business outcome

Using generative AI on AWS, Launchmetrics has delivered benefits across multiple fronts. It has sped up prototype development from as long as 5 months to just a few weeks while increasing the quality of its supervised learning tools. Moreover, the company has gained the ability to create synthetic data sets for new brands that have little online presence. “This gives us the capability to move from managing hundreds of brands through AI to thousands, which is a huge change for us,” says Parés.

  • Speed to Market

Solution

Launchmetrics has been building on Amazon Web Services (AWS) and using machine learning (ML) solutions, including large language models (LLMs), for years. The company stores and organizes all its data in a data lake on Amazon Simple Storage Service (Amazon S3), an object storage service built to retrieve virtually any amount of data from anywhere. When generative AI services became available on AWS, Launchmetrics jumped right in and began using Amazon Bedrock , a fully managed service for building and scaling generative AI applications with foundation models. “With generative AI, what we’ve seen is that it’s an excellent resource to help us improve the interpretation of the model results,” says Pau Montero Parés, chief technology officer of Launchmetrics. It processes and stores up to 7 million documents a day in this data lake. Launchmetrics designed a two-step process to analyze brand data. It also delivers valuable insights into concepts

Data flow

processing runs on Amazon Bedrock; results and state are stored in Amazon S3 for the document processing workload.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon Bedrock Runs core compute or model execution for the document processing workload.
  2. Storage
    • Amazon S3 Stores application, analytics, or workflow state for the document processing solution.

Evidence from the source

By processing tens of millions of chunks of text, Launchmetrics picks up on subtle changes in public perception.
First, it uses ML to identify keywords used to describe brands online.
It then uses LLMs to score the trends it identifies by connecting them to brand dimensions.
Launchmetrics designed a two-step process to analyze brand data.
That’s the challenge that Launchmetrics , a marketing company driven by artificial intelligence (AI), helps fashion, lifestyle, and beauty brands resolve.
When generative AI services became available on AWS, Launchmetrics jumped right in and began using Amazon Bedrock , a fully managed service for building and scaling generative AI applications with foundation models. “With generative AI, what we’ve seen is that it’s an excellent resource to help us improve the interpretation of the model results,” says Pau Montero Parés, chief technology officer of Launchmetrics.
Transparency Evidence & methodology 85/100 reference value · 92% confidence · 2 sources

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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 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 transparency9/10
    2 source references 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

    Launchmetrics customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/launchmetrics-case-study/
    Source type
    Vendor-published customer story
    Published
    Mar 21, 2024
    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
  2. Built In

    Launchmetrics Careers, Perks + Culture | Built In

    Independent secondary
    https://builtin.com/company/launchmetrics

    The Organization JSON-LD lists streetAddress "115 East 23rd Street", addressLocality "New York", addressCountry "US", and numberOfEmployees value 256.

    Source type
    Structured company profile
    Supports
    • Company size
    • Region

Date evidence

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

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