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AWS Financial Services United States

MarketReader

Real-time Analytics

MarketReader is an artificial intelligence (AI) analytics platform providing the financial sector with data-driven explanations of real-time asset movement. During development, MarketReader experienced delays in data delivery and received incomplete datasets from its initial data provider—which reduced the quality of the platform’s insights.

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100 percent
100% uptime
8 months to launch
98 percent

Reported impact

Business outcome

Reliable, Detailed Data Takes MarketReader Live Only eight months after Begole first started ingesting data into MarketReader from Nasdaq, the platform went live. “Once we got the Nasdaq data into the system, the quality went up significantly,” Begole said. “We are able to stand behind our analysis without needing to read every single output.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

MarketReader is an artificial intelligence (AI) analytics platform providing the financial sector with data-driven explanations of real-time asset movement. During development, MarketReader experienced delays in data delivery and received incomplete datasets from its initial data provider—which reduced the quality of the platform’s insights. Combining Quality Data and Fast Delivery for Outstanding Insights Begole had already built a prototype of MarketReader on AWS, which would allow him to convert it to a highly performant, easily scalable product without a major migration. As he searched for a better data solution to support the live product, many of the research market data providers he considered sourced market data from Nasdaq. Recognizing the cost inefficiency of paying for an intermediary service, Begole decided to go directly to the source. Nasdaq proved to be an ideal fit for MarketReader for several reasons.

Solution architecture

3 components · 2 layers
  1. Ingest
    • Amazon Managed Streaming for Apache Kafka Ingests streaming events and meter or application data in near real time.
    • Apache Kafka Connect Moves streaming events into the storage layer for analytics and ML.
  2. Storage
    • Amazon S3 Stores documents, media, telemetry, or training data durably at scale.

Architecture clues

  • To ingest and process event streams, MarketReader uses Amazon Managed Streaming for Apache Kafka (Amazon MSK).
  • With more detailed data served in real time—and a 100 percent uptime—MarketReader has been able to consistently produce the timely, highly informed insights it promises customers.
  • “The Kafka-based streaming is highly performant, so we’re able to get those trades as soon as they come in.

Evidence from the source

As he searched for a better data solution to support the live product, many of the research market data providers he considered sourced market data from Nasdaq.
Combining Quality Data and Fast Delivery for Outstanding Insights Begole had already built a prototype of MarketReader on AWS, which would allow him to convert it to a highly performant, easily scalable product without a
During development, MarketReader experienced delays in data delivery and received incomplete datasets from its initial data provider—which reduced the quality of the platform’s insights.
MarketReader is an artificial intelligence (AI) analytics platform providing the financial sector with data-driven explanations of real-time asset movement.
Missing, Delayed Data Keeps MarketReader from Launching Based in New York City, MarketReader aggregates and analyzes real-time data to help investors, wealth managers, and brokerage firms make more informed decisions.
Using AI-driven models, MarketReader monitors market assets for price movements and provides data-driven explanations of their causes.
Transparency Evidence & methodology 91/100 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 value91/100
  • 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 impact25/25
    2 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

    MarketReader customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/marketreader-nasdaq/
    Source type
    Vendor-published customer story
    Published
    Feb 14, 2025
    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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