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Huron

Document Processing · Enterprise Knowledge Search

Huron is using generative artificial intelligence (AI) to transform the process of launching new clinical trials, a critical component of bringing medical breakthroughs to market. Clinical trials determine whether a planned medical intervention or therapy will have enough financial benefit to validate the investment in research and development, marketing, and production.

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Reported impact

Business outcome

In August 2024, Huron began to deploy its solution that is powered by Amazon Bedrock, significantly shortening turnaround times for launching clinical trials. “We can type a question, type a follow-up question, and query all those documents at once,” says Cody. “And that’s the perfect picture of creating efficiency.

  • Productivity Improvement
  • Speed to Market

Solution

Huron is using generative artificial intelligence (AI) to transform the process of launching new clinical trials, a critical component of bringing medical breakthroughs to market. Clinical trials determine whether a planned medical intervention or therapy will have enough financial benefit to validate the investment in research and development, marketing, and production. At first, Huron’s AI team experimented with a proof of concept that was based on predictive modeling, which uses algorithms to analyze historical data and predict outcomes. But Huron realized that the complexity of unstructured data and the need for more nuanced answers warranted another approach. So it shifted to generative AI, first experimenting with a large language model (LLM) from a leading AI provider and building the support infrastructure from scratch.

Data flow

Data is stored in Amazon OpenSearch Service and processed by Knowledge Bases for Amazon Bedrock, Amazon Bedrock.

Solution architecture

3 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
  2. Storage
    • Amazon OpenSearch Service Stores and retrieves indexed or vectorized content for search and RAG workflows.
  3. Serving
    • Knowledge Bases for Amazon Bedrock Indexes enterprise content so the application can ground responses on internal knowledge.

Architecture clues

  • Additionally, Huron’s AI team improves efficiency and reduces complexity by using Amazon Bedrock native capabilities for handling chunking, vector storage, and information retrieva
  • Huron further improves search functionality using Amazon OpenSearch Serverless , which delivers search and log analytics without provisioning and adjusting resources.
  • The team had to write code for chunking—the breaking down of complex, lengthy documents into smaller segments for the model to process—and manage the vector store database that hou
  • Through the built-in functionality of Amazon Bedrock Knowledge Bases , which lets organizations deliver more relevant, accurate, and customized responses, Huron’s AI team has cut i

Evidence from the source

At first, Huron’s AI team experimented with a proof of concept that was based on predictive modeling, which uses algorithms to analyze historical data and predict outcomes.
But Huron realized that the complexity of unstructured data and the need for more nuanced answers warranted another approach.
Clinical trials determine whether a planned medical intervention or therapy will have enough financial benefit to validate the investment in research and development, marketing, and production.
Huron is using generative artificial intelligence (AI) to transform the process of launching new clinical trials, a critical component of bringing medical breakthroughs to market.
Huron specializes in strategic consulting, technology, and analytics solutions for clients primarily in healthcare and higher education.
It helps large research organizations reduce risk, manage costs, and address compliance for clinical trials.
Transparency Evidence & methodology 66/100 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 value66/100
  • 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 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

    Huron customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/huron-consulting-group-amazon-bedrock-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 21, 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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