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AWS Education India

Indian Institute of Hotel Management

Document Processing

IIHM has increasingly faced challenges related to interviewing in addition to assessing and creating educational content. For instance, IIHM recognized the opportunity to modernize and enhance its processes to align with the evolving needs of students and the education sector.

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Additionally, Talent Talker delivers detailed, data-driven insights on candidates to enable objective hiring with 90 percent accuracy.
Amazon Bedrock's capabilities have been transformative, helping us achieve 30 percent better model performance while continuously evolving our AI solutions for education.
For generative AI capabilities, IIHM relies on Amazon Bedrock and Anthropic’s Claude 3.5 Haiku model.
Surveys indicate that 75 percent of educators and students are feeling more secure interacting with AI-generated content, which helps foster a more engaging learning environment.

Reported impact

Business outcome

Identifying Candidates Faster and Enhancing Education IIHM met its business objectives for interviewing by implementing the AWS generative AI solution. For instance, the solution streamlined and automated the recruitment process to make interviews 50 percent faster, helping the institute more efficiently screen and identify the best candidates for hospitality roles. Key reported metrics include faster interviews through automated recruiting process 50%; hiring accuracy from data-driven insights 90%; higher student job placement rates 20-30%.

  • Productivity Improvement
  • Risk Reduction

Solution

Learn how the Indian Institute of Hotel Management identifies candidates faster and offers a better learning experience on AWS. The Indian Institute of Hotel Management (IIHM) specializes in hospitality education that offers high-quality training and effective placement solutions. Based in India, the institute worked with AWS Partner Workmates to implement an AWS generative AI solution based on Amazon Bedrock. As a result, IIHM is seeing 50 percent faster interviews, 90 percent hiring accuracy, and a 20–30 percent higher rate of student job placements. In addition, surveys show that IIHM has created a more engaging learning environment, with 75 percent of educators and students now feeling more secure interacting with AI-generated content.

Data flow

Source data is ingested through Amazon Transcribe, Amazon Textract, stored or indexed in Amazon S3, processed with Amazon Bedrock, Amazon EC2, Claude 3, coordinated by AWS Auto Scaling.

Solution architecture

10 components · 5 layers
  1. Ingest
    • Amazon Transcribe Transcribes call audio or speech into text for downstream analysis.
    • Amazon Textract Extracts structured information from business or clinical documents.
  2. Compute
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Amazon EC2 Provides configurable compute capacity for application, training, or inference workloads.
    • Claude 3 Serves as the primary large language model for generation or analysis tasks.
  3. Storage
    • Amazon S3 Stores raw, intermediate, or curated files and datasets.
  4. Orchestration
    • AWS Auto Scaling Adjusts capacity automatically as demand changes.
  5. Governance
    • AWS IAM Controls authentication and authorization across AWS resources.
    • Amazon VPC Isolates workloads and network segments inside secure private cloud boundaries.
    • AWS KMS Manages encryption keys for protected data and services.

Architecture clues

  • AWS Auto Scaling manages changes in load and ensures the continuous availability of all services.
  • Amazon Bedrock enhanced Claude 3.5 Haiku by providing a scalable and secure environment to simplify deployment, with Amazon Bedrock pre-built integrations enabling the rapid experimentation and customization of the model
  • IIHM also secures APIs that integrate the AI chatbot with other systems by using authentication tokens and ensuring that API endpoints are protected from unauthorized access.
  • The new solution takes advantage of Amazon Elastic Compute Cloud (Amazon EC2) instances to host the API server and other application services, and it uses Amazon Simple Storage Service (Amazon S3) for storing educational

Evidence from the source

Additionally, Talent Talker delivers detailed, data-driven insights on candidates to enable objective hiring with 90 percent accuracy.
Amazon Bedrock's capabilities have been transformative, helping us achieve 30 percent better model performance while continuously evolving our AI solutions for education.
For generative AI capabilities, IIHM relies on Amazon Bedrock and Anthropic’s Claude 3.5 Haiku model.
IIHM has increasingly faced challenges related to interviewing in addition to assessing and creating educational content. For instance, IIHM recognized the opportunity to modernize and enhance its processes to align with the evolving needs
Surveys indicate that 75 percent of educators and students are feeling more secure interacting with AI-generated content, which helps foster a more engaging learning environment.
Using AWS Generative AI to Streamline Key Processes IIHM chose to move its IT environment to Amazon Web Services (AWS) to overcome its challenges, working with Workmates, anjgjgy AWS Generative AI Competency Partner , to build and deploy a
Transparency Evidence & methodology 95/100 reference value · 90% confidence · 2 sources

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 value95/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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

    Indian Institute of Hotel Management customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/iihm-workmates/
    Source type
    Vendor-published customer story
    Published
    Feb 18, 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
  2. LinkedIn

    IIHM | LinkedIn

    Independent secondary
    https://in.linkedin.com/company/iihm

    The profile markup includes "addressCountry":"IN"" and "numberOfEmployees":{..."value":634}, which maps to Mid-market.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

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

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