Elm AI (Formerly Esger

About Elm AI (Formerly Esger

Provides an AI-powered platform that automates supplier due diligence by analyzing audit reports and compliance documents against company codes of conduct, including RBA, SLCP, BSCI, and SMETA. The system generates risk scores, corrective action plans, and actionable insights from real supplier data, reducing review time to under 10 minutes and enabling organizations to manage sustainability and compliance more efficiently.

```xml <problem> Responsible sourcing teams face challenges in efficiently and accurately conducting supplier due diligence, often spending significant time manually reviewing audit reports and compliance documents. This process is further complicated by the need to adhere to various industry frameworks and company-specific codes of conduct, making it difficult to identify non-conformances and assess supplier risk effectively. </problem> <solution> AuditSense AI, powered by the Elm AI Engine, automates supplier due diligence by analyzing audit reports and compliance documentation against company codes of conduct, including standards such as RBA, SLCP, BSCI, and SMETA. The platform leverages a proprietary large language model (LLM) to identify non-conformances, generate risk scores, and create contextual corrective action plans tailored to specific supplier challenges and brand guidelines. By transforming social audits into actionable insights, AuditSense AI enables organizations to evaluate and enhance their supply chains' social and environmental impact with increased speed and accuracy. The system streamlines the review process, reducing the time required to under 10 minutes per report, and provides a comprehensive supply chain insight dashboard for improved decision-making. </solution> <features> - AI-powered analysis of supplier audit reports and compliance documents - Automated identification of non-conformances based on company-specific codes of conduct - Support for major industry frameworks, including RBA, SLCP, SMETA, BSCI, and WRAP - AI-generated risk scores and corrective action plans - Customizable SAQs (Self-Assessment Questionnaires) with AI validation - Supply chain insight dashboard for visualizing trends and granular insights - SOC 2 certified data security - Patent-pending LLM technology </features> <target_audience> The primary target audience includes responsible sourcing teams, compliance managers, and sustainability professionals seeking to streamline supplier due diligence, improve supply chain transparency, and reduce the risk of non-compliance. </target_audience> ```

What does Elm AI (Formerly Esger do?

Provides an AI-powered platform that automates supplier due diligence by analyzing audit reports and compliance documents against company codes of conduct, including RBA, SLCP, BSCI, and SMETA. The system generates risk scores, corrective action plans, and actionable insights from real supplier data, reducing review time to under 10 minutes and enabling organizations to manage sustainability and compliance more efficiently.

Where is Elm AI (Formerly Esger located?

Elm AI (Formerly Esger is based in East New York, United States.

When was Elm AI (Formerly Esger founded?

Elm AI (Formerly Esger was founded in 2023.

How much funding has Elm AI (Formerly Esger raised?

Elm AI (Formerly Esger has raised 250000.

Location
East New York, United States
Founded
2023
Funding
250000
Employees
6 employees

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Elm AI (Formerly Esger

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Executive Summary

Provides an AI-powered platform that automates supplier due diligence by analyzing audit reports and compliance documents against company codes of conduct, including RBA, SLCP, BSCI, and SMETA. The system generates risk scores, corrective action plans, and actionable insights from real supplier data, reducing review time to under 10 minutes and enabling organizations to manage sustainability and compliance more efficiently.

elm-ai.com700+
Founded 2023East New York, United States

Funding

$

Estimated Funding

$100K+

Team (5+)

No team information available.

Company Description

Problem

Responsible sourcing teams face challenges in efficiently and accurately conducting supplier due diligence, often spending significant time manually reviewing audit reports and compliance documents. This process is further complicated by the need to adhere to various industry frameworks and company-specific codes of conduct, making it difficult to identify non-conformances and assess supplier risk effectively.

Solution

AuditSense AI, powered by the Elm AI Engine, automates supplier due diligence by analyzing audit reports and compliance documentation against company codes of conduct, including standards such as RBA, SLCP, BSCI, and SMETA. The platform leverages a proprietary large language model (LLM) to identify non-conformances, generate risk scores, and create contextual corrective action plans tailored to specific supplier challenges and brand guidelines. By transforming social audits into actionable insights, AuditSense AI enables organizations to evaluate and enhance their supply chains' social and environmental impact with increased speed and accuracy. The system streamlines the review process, reducing the time required to under 10 minutes per report, and provides a comprehensive supply chain insight dashboard for improved decision-making.

Features

AI-powered analysis of supplier audit reports and compliance documents

Automated identification of non-conformances based on company-specific codes of conduct

Support for major industry frameworks, including RBA, SLCP, SMETA, BSCI, and WRAP

AI-generated risk scores and corrective action plans

Customizable SAQs (Self-Assessment Questionnaires) with AI validation

Supply chain insight dashboard for visualizing trends and granular insights

SOC 2 certified data security

Patent-pending LLM technology

Target Audience

The primary target audience includes responsible sourcing teams, compliance managers, and sustainability professionals seeking to streamline supplier due diligence, improve supply chain transparency, and reduce the risk of non-compliance.

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