AWS Media and Entertainment Chile

Megamedia

Customer Service Automation · Enterprise Knowledge Search

As access to generative AI expands, companies have the potential to use the technology to improve people’s lives. Chilean media holding company Megamedia chose to use generative AI to make it simpler for people to find information about government support programs.

30 seconds to access information
4 months
4 months to build and deploy models
80 percent

Reported impact

Business outcome

Now that Megamedia has created this chatbot, it has the potential to expand the solution to develop models trained on other specific data sources. For example, it could create a chatbot specifically trained on information about the World Cup or on political news.

Solution

As access to generative AI expands, companies have the potential to use the technology to improve people’s lives. Chilean media holding company Megamedia chose to use generative AI to make it simpler for people to find information about government support programs. Because it is powered by large language models (LLMs), interacting with the Dato Útil chatbot feels like a real conversation, a much better user experience than chatbots with only static, preloaded answers. The chatbot is built using Claude 2, an LLM from Anthropic, through Amazon Bedrock , a fully managed service that offers a choice of high-performing foundation models from well-known AI companies. ARKHO provided expertise in Amazon Bedrock, which is integrated within Generative AI Application Builder on AWS. “We use Amazon Bedrock for basically all our generative AI use cases because it is very flexible,” says Nicolas Caraball, data scientist at ARKHO.

Data flow

Application logic runs on Amazon Bedrock and is surfaced through Amazon Kendra.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
  2. Serving
    • Amazon Kendra Indexes content and returns relevant enterprise search results.

Architecture clues

  • In 4 months, Megamedia created, tested, and launched a chatbot that can save people hours of searching for information.
  • In early 2024, Megamedia decided to use generative AI to build a chatbot that could help people answer their questions about government benefits programs.
  • The Dato Útil chatbot went live on meganoticias.cl, officially launching at the beginning of May 2024, just 4 months after the beginning of the project.
  • Together, Megamedia and ARKHO refined the model accuracy for the chatbot and migrated the model to Megamedia’s website.
  • With a chatbot, for example, an unemployed parent looking for information about both childcare and unemployment benefits could ask questions about their specific situation and get

Evidence from the source

As access to generative AI expands, companies have the potential to use the technology to improve people’s lives.
As one of the largest broadcasters in Chile, Megamedia has websites as well as radio, television, and cable channels in the region.
Chilean media holding company Megamedia chose to use generative AI to make it simpler for people to find information about government support programs.
For example, it could create a chatbot specifically trained on information about the World Cup or on political news.
Now that Megamedia has created this chatbot, it has the potential to expand the solution to develop models trained on other specific data sources.
The company’s news website, meganoticias.
Transparency Evidence & methodology 72/100 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 value72/100
  • 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 specificity12/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    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

    Megamedia customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/megamedia-case-study/
    Source type
    Vendor-published customer story
    Published
    Aug 23, 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

Date evidence

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