AI Buyer Insights:

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Michelin, an e2open customer evaluated Oracle Transportation Management

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Michelin, an e2open customer evaluated Oracle Transportation Management

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

List of LearningMate Kadal AI Customers

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Logo Customer Industry Empl. Revenue Country Vendor Application Category When SI Insight Insight Source
F.A. Davis Company Professional Services 6 $1M United States LearningMate LearningMate Kadal AI AI Frameworks and Libraries 2024 n/a In 2024, F.A. Davis Company partnered with LearningMate to deploy LearningMate Kadal AI as the core of a new digital product infrastructure for nursing and health science content. The implementation targeted the publishing and content development function in the United States and delivered multiple digital titles while streamlining content development workflows. LearningMate Kadal AI was applied to automate content ingestion and conversion, provide semantic enrichment and metadata normalization, and support AI assisted authoring and editorial review workflows, reflecting capabilities consistent with the AI Frameworks and Libraries category. Configuration emphasized template driven publishing pipelines and modular content components to accelerate title assembly and reuse across product editions. Operational workstreams focused on embedding Kadal content tools into F.A. Davis editorial workflows and content development pipelines, with content teams and product managers as primary users. The engagement prioritized automated content packaging for digital formats and orchestration of review cycles rather than named third party system integrations. Governance and process changes included revised content quality checkpoints and standardized metadata governance to support repeatable title production and downstream distribution, with rollout executed as a publishing and content engagement across multiple digital titles. The application of LearningMate Kadal AI in this engagement is inferred from vendor materials referencing Kadal content tools rather than being explicitly named in the F.A. Davis case study.
Midatahub United States Education 25 $4M United States LearningMate LearningMate Kadal AI AI Frameworks and Libraries 2013 Double Line In 2013 Midatahub United States engaged LearningMate with system integrator Double Line to deploy LearningMate Kadal AI as an AI Frameworks and Libraries solution for Michigan’s statewide K-12 data integration and analytics hub. The initiative established a centralized Ed-Fi data integration and analytics architecture to consolidate district reporting feeds and standardize student and program data for state reporting and operational analytics. The deployment configured core capabilities around standardized data ingestion and validation, Ed-Fi compliant data modeling, and analytics services. LearningMate Kadal AI was applied as an AI and machine learning workbench to support model development, feature engineering, and orchestration of AI/ML workflows alongside the analytic pipeline, aligning with typical AI Frameworks and Libraries functionality. Integrations centered on Ed-Fi data exchange with district student information systems and state reporting feeds to populate the hub, supporting analytics and reporting use cases across the K-12 public education domain. Operational coverage was statewide in Michigan, and the implementation supported data teams within the state education agency and district analytics groups. Governance emphasized centralized data governance, standardized data contracts, and workflow automation for data submission and validation coordinated by the state. The program produced an explicitly stated estimated annual savings of more than $41 million and an 830 percent return on investment according to the vendor study, outcomes reported for the public sector engagement in Michigan.
New Mexico Government 27023 $35.8B United States LearningMate LearningMate Kadal AI AI Frameworks and Libraries 2025 n/a In 2025, New Mexico engaged LearningMate to design and build a multi agency P20W+ longitudinal data system connecting ECECD, PED, HED and DWS. The deployment centered on LearningMate Kadal AI, classified as AI Frameworks and Libraries, to provide a platform for statewide cross agency analytics and student lifecycle intelligence. The implementation included a P20W+ common data model, ingest and ETL pipelines, probabilistic record matching and identity resolution capabilities, and an analytical layer for cross agency reporting and cohort analysis. LearningMate Kadal AI was used to enable AI assisted analytics workflows and model orchestration, an interpretation inferred from the vendor case study rather than explicitly declared by the state. Integrations consolidated administrative data feeds from the Early Childhood Education and Care Department, the Public Education Department, the Higher Education Department and the Department of Workforce Solutions, enabling longitudinal linking across pre K through workforce. Operational coverage spanned multiple state agencies across New Mexico and focused on supporting analytics for student outcomes and cross agency policy analysis. Project governance established cross agency data sharing agreements, role based access controls, and an enterprise data governance process to manage matching rules and analytic model deployment. The stated objective of the implementation is to improve student outcomes and cross agency analytics, as reflected in the state engagement and vendor documentation.
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Buyer Intent: Companies Evaluating LearningMate Kadal AI

ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating LearningMate Kadal AI. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating LearningMate Kadal AI for AI Frameworks and Libraries include:

  1. Reso India, a India based Professional Services organization with 20 Employees

Discover Software Buyers actively Evaluating Enterprise Applications

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FAQ - APPS RUN THE WORLD LearningMate Kadal AI Coverage

LearningMate Kadal AI is a AI Frameworks and Libraries solution from LearningMate.

Companies worldwide use LearningMate Kadal AI, from small firms to large enterprises across 21+ industries.

Organizations such as New Mexico, Midatahub United States and F.A. Davis Company are recorded users of LearningMate Kadal AI for AI Frameworks and Libraries.

Companies using LearningMate Kadal AI are most concentrated in Government, Education and Professional Services, with adoption spanning over 21 industries.

Companies using LearningMate Kadal AI are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of LearningMate Kadal AI across Americas, EMEA, and APAC.

Companies using LearningMate Kadal AI range from small businesses with 0-100 employees - 66.67%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 0%, and global enterprises with 10,000+ employees - 33.33%.

Customers of LearningMate Kadal AI include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.

Contact APPS RUN THE WORLD to access the full verified LearningMate Kadal AI customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of AI Frameworks and Libraries.