---
title: "Tech Mahindra M2TS Services Powered by Hyland"
description: "M2TS services are enhanced through collaboration between Tech Mahindra and Hyland, supporting efficient processes and improved service delivery."
url: "https://www.hyland.com/en/partners/tech-mahindra/m2ts-services"
lang: "en"
published: "2026-07-09T16:03:23.731Z"
---

# Tech Mahindra M2TS Services Powered by Hyland

## Machine Learning and Metadata Tagging (M2TS) Services

Tech Mahindra’s “Machine Learning and Metadata Tagging (M2TS) Services” provides Content Recommendation, Automatic Tagging of Content, and Content Organization and increase the findability of Related Content.

Tech Mahindra’s “Machine Learning and Metadata Tagging (M2TS) Services” provides Content Recommendation, Automatic Tagging of Content, and Content Organization and increase the findability of Related Content. Using the Machine Learning and Big Data, M2TS builds topics and corresponding tags for each topic based on a common grouping and prepare Meta Tags. Once a document is imported in Alfresco folder which has integration with M2TS, the “Machine Learning tags” are auto populated to the document’s tag and establish a contextual relation.

## Business challenges

The digital content is growing exponentially ~2,000 Exabytes added in 2016. For large Enterprise organization, finding the right content at the right time make a huge impact on critical decision making and business deals. However, if the documents are not organized and metadata are not tag/defined properly, then it became a nightmare for the DMS users to find the right documents.

- Inconsistent document organization
- Tedious and time consuming manual process to find a document and define tags for each document.
- Insufficient metadata to find it easily
- Human error
- Difficult in organizing large volume of content

## Solution Overview

- To make search as **contextual** and make more relevant, solution was built in **big data/Hadoop** with **Machine Learning technique** for automated and reliable content tagging.
- Enterprise search for indexing about **100 TB** of **structured** and **non-structured data**.
- Open and **generic rest API** provided for faster integration and automated testing
- **Auto classification** done to **tag** the content according to security policies using big data with **machine learning algorithm**
- Content clustering implemented inside big data with machine learning algorithm that finally goes **inside HIVE Database** for **faster traversal**
- Service based integration integrations with Alfresco repository using the M2TS REST Web services.

## How did the solution address the business problem?

- Saves time, increase productivity
- Provide relevant search results quickly
- Automated tagging
- Content Contextual recommendations
- Smart and Intelligent decision making with self-learning ability
- Improve **user experience**
- patterns recognition and organize the content

## Customer Benefits

- Improve productivity: Reduction in document search time
- Improve knowledge management.
- Effective and efficient process management.
- Cost savings by eliminating manual efforts for classification.
- Gain competitive advantage by adapting to the changing knowledge economy.