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.

Digital Business Process Management Services

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.

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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:


CPG

Retail

Media

Rail Transport

Oil & Gas

Insurance

Healthcare

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 and labelling capabilities span several areas, such as:

Text

Tag and classify for NLP and search optimisation use cases

ENTITY TAGGING | LINKING |
CLASSIFICATION | SENTIMENT TAGGING

Image

Classify and segment images for computer vision use cases

BOUNDING BOX | SEGMENTATION |
POLYGONS | CLASSIFICATION

Audio

Transcribe, annotate, and grade speech and audio use cases

TRANSCRIPTION | GRADING |
CLASSIFICATION | SENTIMENT TAGGING

Video

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


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Our investments

Our investments - AI/ML

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Our value propositions

Flexible and scalable managed services operating model

Dedicated and trained annotator pool (no crowdsourcing) that works as an extension of the client’s AI team

Greater than 98% accuracy through robust QA framework

Greater than 30% faster speeds and higher throughputs using AI-assisted annotation tools

Agility to switch between a wide variety of third-party and client proprietary annotation platforms


Request for services

Find out more about how we can help your organization navigate its next. Let us know your areas of interest so that we can serve you better