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AWS Nonprofit Spain

Instituto de Ingeniería del Conocimiento

Real-time Analytics

For nearly 20 years, Instituto de Ingenieria del Conocimiento (IIC) has provided renewable energy prediction services to its clients. The research and development center specializes in artificial intelligence (AI) and other advanced technologies, helping energy companies better understand and optimize their operations.

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20 million
20 years
30 percent
30 years

Reported impact

Business outcome

In the future, IIC plans to continually enhance EA3 for optimal agility and performance. It plans to add new features powered by AWS technologies and explore new use cases, such as incorporating new data sources and tailoring the tool for its clients’ specific needs. As it moves forward, IIC is committed to maintaining its position in the renewable energy sector, evolving EA3 to meet dynamic market demands.

Solution

For nearly 20 years, Instituto de Ingenieria del Conocimiento (IIC) has provided renewable energy prediction services to its clients. The research and development center specializes in artificial intelligence (AI) and other advanced technologies, helping energy companies better understand and optimize their operations. Thanks to the redesign, IIC can now efficiently process diverse data sources through EA3. For effective communication within the architecture, IIC relies on Amazon Simple Notification Service (Amazon SNS), a fully managed Pub/Sub service for A2A and A2P messaging, and Amazon Simple Queue Service (Amazon SQS), a fully managed message queuing for microservices, distributed systems, and serverless applications. The company uses Amazon SNS to manage notifications and Amazon SQS to facilitate asynchronous data delivery, while AWS Lambda is used to run scalable data processing and AI inference.

Data flow

Data flows from AWS Glue, Amazon SNS into Amazon S3, is processed by AWS Lambda.

Solution architecture

7 components · 5 layers
  1. Ingest
    • AWS Glue Prepares and moves data between operational sources and analytics or ML environments.
    • Amazon SNS Publishes notifications between decoupled services.
    • Amazon SQS Buffers asynchronous work between distributed services.
  2. Compute
    • AWS Lambda Runs event-driven application logic and serverless processing tasks.
  3. Storage
    • Amazon S3 Stores documents, media, telemetry, or training data durably at scale.
  4. Serving
    • Amazon API Gateway Exposes application and AI services through managed APIs.
  5. Orchestration
    • AWS Step Functions Coordinates multi-step serverless workflows and automation.

Architecture clues

  • For effective communication within the architecture, IIC relies on Amazon Simple Notification Service (Amazon SNS), a fully managed Pub/Sub service for A2A and A2P messaging, and A
  • It chose to migrate its code to Python and implement an EDA, which relies on serverless functions to dynamically respond to near real-time events by activating specific, automated
  • The company could meet increased data processing demands and scale up resources by adopting a serverless operation model on AWS, particularly the automatic scaling feature of AWS L
  • To deliver AI predictions at scale, IIC completely redesigned its architecture by migrating to Amazon Web Services (AWS) and adopting an event-driven architecture (EDA) powered by
  • To transform data, EA3 relies on serverless functions in AWS Lambda to carry out specific tasks and scale the architecture as needed.

Evidence from the source

Based in Spain, IIC has over 30 years of experience in big data analysis, machine learning, and natural language processing.
For effective communication within the architecture, IIC relies on Amazon Simple Notification Service (Amazon SNS), a fully managed Pub/Sub service for A2A and A2P messaging, and Amazon Simple Queue Service (Amazon SQS),
For nearly 20 years, Instituto de Ingenieria del Conocimiento (IIC) has provided renewable energy prediction services to its clients.
In 2021, the company confronted a critical challenge that affected its ability to deliver AI predictions at scale.
Thanks to the redesign, IIC can now efficiently process diverse data sources through EA3.
The research and development center specializes in artificial intelligence (AI) and other advanced technologies, helping energy companies better understand and optimize their operations.
Transparency Evidence & methodology 62/100 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 value62/100
  • 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 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
    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 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

    Instituto de Ingeniería del Conocimiento customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/iic-serverless-case-study/
    Source type
    Vendor-published customer story
    Published
    Apr 4, 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.

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