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Snowflake Commercial and Professional Services India

LTM

Risk Management

replace fragmented HR systems and manual hiring workflows that made it hard to predict onboarding risk, reduce dropouts, and scale AI-driven recruiting efficiently.

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10x Faster query performance on critical hiring data with Snowpark
70% TCO savings achieved from migrating to Snowflake
80% accuracy in candidate onboarding predictions
develops and deploys AI/ML applications five times faster than before

Reported impact

Business outcome

LTM achieved 80% accuracy in candidate onboarding predictions, improved critical query performance 10x, reduced TCO by 70%, and accelerated AI/ML delivery by 5x.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

LTM uses Snowflake, Snowpark, and Cortex AI on Snowflake to replace fragmented hr systems and manual hiring workflows that made it hard to predict onboarding risk, reduce dropouts, and scale ai-driven recruiting efficiently. LTM consolidates hiring, onboarding, and enterprise HR data that previously lived in siloed systems and manual processes onto Snowflake. Snowpark runs the core analytics and model logic for joiner prediction, while Cortex AI helps the team build AI-driven recruiting experiences without maintaining brittle monolithic code. HR and recruiting teams use the unified platform to predict post-offer dropouts earlier, tailor engagement, and ship new AI/ML hiring applications faster. Keeping ingestion, storage, transformation, and downstream access on one governed platform reduces data copying, simplifies operations, and gives teams a single trusted operating view for analytics and AI. LTM achieved 80% accuracy in candidate onboarding predictions, improved critical query...

Data flow

Hiring, onboarding, and HR data flow into Snowflake, Snowpark, and Cortex AI; curated data and model outputs are then served to HR leaders, recruiters, and AI/ML applications.

Solution architecture

3 components · 2 layers
  1. Compute
    • Snowpark Runs transformations, analytics, and application logic directly where Snowflake data already lives.
    • Cortex AI Provides in-platform AI functions used for summarization, classification, generation, or model-assisted analysis.
  2. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.

Architecture clues

  • Cortex AI supports AI-driven hiring and onboarding prediction use cases.
  • Snowpark replaced monolithic code paths that struggled with high-volume processing.
  • Unifies fragmented HR and recruitment data on one platform.

Evidence from the source

10x Faster query performance on critical hiring data with Snowpark
70% TCO savings achieved from migrating to Snowflake
80% accuracy in candidate onboarding predictions
leveraging Snowflake Cortex and Snowpark, LTM now develops and deploys AI/ML applications five times faster than before
Transparency Evidence & methodology 100/100 reference value · 79% 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 value100/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Snowflake

    LTM customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/ltm/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived May 18, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 9, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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