Fusion CX Limited has been named the Winner in the Best Data Labeling / Annotation Platform category at the 2nd Edition of the ET Enterprise AI Awards 2026, held in Bengaluru on September 17, 2026.
The ET Enterprise AI Awards recognize organizations applying artificial intelligence to solve business and technology challenges. Fusion CX received the award for its work in data labeling and annotation, an area that has become increasingly important as organizations develop more capable AI models across text, image, audio, video, computer vision, and other data types.
The recognition also marks an important milestone for Annotera, Fusion CX’s AI data infrastructure platform.
Building the Data Foundation Behind Modern AI
Behind every capable AI system is a large volume of data that must be collected, structured, labeled, reviewed, and continuously improved.
As AI models become more sophisticated, the requirements for training data are also changing. Organizations increasingly need more than large datasets. They need accurate annotations, domain-specific human judgment, multimodal data processing, model-response evaluation, and quality controls that can operate at scale.
Through Annotera, Fusion CX is building infrastructure to support these requirements.
From multimodal data annotation and reinforcement learning from human feedback (RLHF) to computer vision and robotics, Annotera helps create the data infrastructure needed to train AI systems that can understand, learn, and operate in increasingly complex environments.
The platform supports organizations working with different forms of training and evaluation data, including text, images, audio, video, and other multimodal datasets.
From Data Annotation to AI Data Infrastructure
The role of data annotation is expanding as AI moves beyond traditional supervised machine-learning applications.
Large language models, multimodal AI, autonomous systems, computer vision applications, and robotics increasingly require human input at different stages of the AI development lifecycle.
This can include labeling raw datasets, evaluating model responses, comparing outputs, identifying errors, applying domain-specific judgment, validating model behavior, and refining datasets based on how models perform.
Annotera brings these workflows together with a focus on quality, scalability, and human expertise.
The aim is to help AI companies and enterprises move from raw information to training-ready and evaluation-ready data while maintaining consistent quality across large and complex projects.
Supporting Multimodal and Advanced AI Use Cases
Modern AI systems are increasingly being trained to work across several types of information at once.
A single AI application may need to understand written instructions, identify objects in images, interpret speech, process video sequences, or make decisions based on interactions with physical environments.
These applications create new requirements for how training data is prepared.
Annotera supports data workflows across areas such as:
- Text, image, audio, and video annotation
- Multimodal dataset preparation
- RLHF and human preference data
- Model response evaluation
- Computer vision annotation
- Data preparation for robotics and embodied AI
- Human review and quality assurance
- Domain-specific annotation and evaluation
By combining technology with trained human reviewers, Fusion CX is developing Annotera to support the increasingly specialized data requirements of AI developers and enterprises.
Human Expertise Remains Central to AI Development
While AI can automate parts of the data preparation process, human judgment remains important where models need to understand context, intent, accuracy, relevance, safety, and domain-specific information.
This becomes especially important when datasets involve complex decisions rather than simple classification.
Annotera’s approach combines structured workflows and technology with human expertise, enabling organizations to introduce human review at the points where judgment and contextual understanding matter most.
For Fusion CX, this capability also builds on its experience managing large-scale, people-led operations across global markets.
Recognition for the Teams Behind Annotera
The Best Data Labeling / Annotation Platform award at the ET Enterprise AI Awards 2026 recognizes the work of the teams developing and operating Annotera as Fusion CX expands its capabilities in AI data infrastructure.
The recognition comes as enterprises and AI developers are placing greater attention on the quality of the data used to train, evaluate, and improve AI systems.
For Fusion CX, Annotera represents an extension of its technology and operations capabilities into the infrastructure layer supporting AI development.
As models continue to become more capable and multimodal, Fusion CX plans to continue developing Annotera around the changing data, evaluation, and human-feedback requirements of modern AI systems.
