AWS Education Belgium

Amanote

Enterprise Knowledge Search

Amanote is helping transform learning in both higher-education and staff training scenarios. Its app makes it easy to add notes and comments to coursework.

2 Minutes to onboard new customers
3 Million+ monthly queries handled per customer
AWS Lambda sits at the core of the app, handling the 1–3 million queries per customer, per month, that are generated.
Amanote Optimizes Note-Taking App for Learners, Handling Millions of Queries on AWS Amanote can onboard new large university customers for its note-taking app in less than 2...

Reported impact

Business outcome

Amanote now has over 500,000 users in 50 countries across four continents, including Asia, Europe, and the US. With a solid app platform, the company is now launching on AWS Marketplace .

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Belgium-based educational technology (EdTech) business Amanote created a note-taking app that's used by students to annotate course materials. With plans to grow its customer base in higher-education facilities, it wanted to make sure its infrastructure could grow with customer demand. From the start, Amanote decided to build on Amazon Web Services (AWS) to benefit from its comprehensive set of serverless services, helping it to scale rapidly and support growth. Using AWS, Amanote can onboard a new university customer in 2 minutes and accommodate thousands of users in just a few hours. It has achieved this by building the app's backend and multiple features on AWS Lambda so it can run code without thinking about servers or clusters.

Data flow

From the start, Amanote has used a cost-effective AWS database solution to store document metadata and manage licensing using Amazon DynamoDB , a serverless, NoSQL, fully managed database with single-digit millisecond performance at any scale. It is also starting to test ML enriched data streams, using Amazon Kinesis Data Firehose , to load near-real-time streams into analytics services.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon Bedrock Runs the foundation model layer for the knowledge search and question-answering workflows.
    • AWS Lambda Executes serverless processing and workflow glue for the knowledge search and question-answering workflows.
  2. Storage
    • Amazon DynamoDB Stores high-scale application state and metadata for the knowledge search and question-answering workflows.
  3. Serving
    • Amazon Q Assists teams and workflows that support the knowledge search and question-answering workflows.
    • Amazon API Gateway Exposes APIs that front the knowledge search and question-answering workflows.

Architecture clues

  • "Using AWS serverless services means we can free up time to focus on what matters—developing and growing the business." All API requests are sent first to Amazon API Gateway , which the company can...
  • From the start, Amanote decided to build on Amazon Web Services (AWS) to benefit from its comprehensive set of serverless services, helping it to scale rapidly and support growth.
  • From the start, Amanote has used a cost-effective AWS database solution to store document metadata and manage licensing using Amazon DynamoDB , a serverless, NoSQL, fully managed database with...
  • It is also starting to test ML enriched data streams, using Amazon Kinesis Data Firehose , to load near-real-time streams into analytics services.
  • With pay-as-you-go services from AWS, the company can focus on optimizing service use to keep costs low and also minimize the number of API calls made.

Evidence from the source

Amanote helps educators and instructors to deliver more interactive lessons, add oral explanations, and gain feedback to help improve course content.
From the start, Amanote decided to build on Amazon Web Services (AWS) to benefit from its comprehensive set of serverless services, helping it to scale rapidly and support growth.
It has achieved this by building the app's backend and multiple features on AWS Lambda so it can run code without thinking about servers or clusters.
This has not only minimized costs, but also improved quality of service and app performance for end users.
Using AWS, Amanote can onboard a new university customer in 2 minutes and accommodate thousands of users in just a few hours.
With pay-as-you-go services from AWS, the company can focus on optimizing service use to keep costs low and also minimize the number of API calls made.
Transparency Evidence & methodology 96/100 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 value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 impact25/25
    2 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

    Amanote customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/amaonote-case-study/
    Source type
    Vendor-published customer story
    Published
    Aug 29, 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.