Annotation services for AI/ML

Artificial intelligence (AI) and deep learning technologies are powering the next wave of innovation. Global enterprises are increasingly integrating self-learning AI/ML models into their ecosystems to vastly improve operational efficiency, augment human capability, and positively impact the lives of customers. But for any machine-learning (ML) model to succeed, vast volumes of meaningful data are required to ‘train’ the model through a repetitive and time-consuming annotation and labelling process.

data annotation services

Annotation Service - Training data at scale


At Infosys BPM, we help client data science teams build high-quality ‘training data’ for AI at scale, using a platform plus human-in-the-loop service model, which saves time and resources for the teams and focuses on strategic priorities like refining and improving the AI model itself. Our proven service model leverages the power of human intelligence (humanware) and automation capability (software) to continuously churn out high-quality training datasets at scale for AI training and evaluation.


Our credentials

We work with several marque brands across the globe in their Al/ML journey and support use cases for several key clients, such as:

  • Autonomous driving:

    HD image and point cloud annotations for a global technology company
  • Virtual assistant devices:

    NLP annotation and audio grading for a US-based hi-tech leader
  • Customer support:

    Conversation generation, categorisation, and intent tagging for a leading US-based telecommunications major
  • Mining (autonomous trucks):

    Drone image segmentation and feature extraction for an Australian mining giant

Infosys BPM supports AI/ML use cases for the following sectors:




Rail Transport

Oil & Gas



Financial Services

We are experienced in handling image annotations, work packages, and audio files for clients across the globe, which include:

  • 28+ million work packages managed per year
  • 150+ million image annotations handled per month
  • 1.2+ million audio files processed per month

Accelerating AI/ML projects through a hybrid model

Our agile operating model enables us to seamlessly work across different annotation platforms in a platform-agnostic way, whether leveraging client-developed in-house tools, open source platforms, or third-party platforms. Here is an overview of the annotation process that is typically followed for creating training data.

ML projects through a hybrid model

Our tie-ups with industry-leading training data platforms allow us to combine human capability with state-of-the-art intelligent automation to increase speed to market and deliver >98% accurate training data output.


Our annotation service and labelling capabilities span several areas, such as:


Tag and classify for NLP and search optimisation use cases

Entity Tagging | Linking | Classification | Sentiment Tagging


Classify and segment images for computer vision use cases

Bounding Box | Segmentation | Polygons | Classification


Transcribe, annotate, and grade speech and audio use cases

Transcription | Grading | Classification | Sentiment Tagging


Annotate key frames and moving objects in a video format

Bounding Box | Segmentation | Polygons | Key Points

Sensor data

Annotate data from sensors
and loT devices

Lidar Time Series Tagging | Pattern Tagging | 3d Point Cloud

Improve efficiency with AI/ML models

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