Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.

Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.

Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.

Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.

Data Canvas

Data Canvas

Data Canvas

The Unified Command Center

The Unified Command Center

The Unified Command Center

Powering AI Data Operations

Powering AI Data Operations

Powering AI Data Operations

Data Canvas is Centific’s next-generation annotation and data transformation platform that turns raw data into model-ready assets. Through end-to-end workflows, from preprocessing to QA and post-processing, it ensures consistent, high-quality data for AI, analytics, and research at scale.

The hidden infrastructure behind world-class AI models

The hidden infrastructure behind world-class AI models

The hidden infrastructure behind world-class AI models

Overview

Overview

The Foundation of AI Performance

The Foundation of AI Performance

Built on Quality.

Built on Quality.

Built on Quality.

Data annotation is the backbone of every AI/ML pipeline; without high-quality labels, models cannot reliably learn patterns, make accurate predictions, or generate meaningful insights.

High-quality training data is created through disciplined processes, expert review, and continuous improvement, not just tools. Data Canvas brings these elements together in a single operational layer, enabling teams to transform raw data into dependable, model-ready assets with consistency, traceability, and scale.

Data annotation is the backbone of every AI/ML pipeline; without high-quality labels, models cannot reliably learn patterns, make accurate predictions, or generate meaningful insights.

High-quality training data is created through disciplined processes, expert review, and continuous improvement, not just tools. Data Canvas brings these elements together in a single operational layer, enabling teams to transform raw data into dependable, model-ready assets with consistency, traceability, and scale.

  • End-to-End Data Transformation

    Raw data must pass through multiple stages before it is usable for machine learning. Data Canvas provides a unified pipeline for preprocessing, annotation, quality assurance, and post-processing; ensuring that every dataset is consistently transformed, validated, and delivered in model-ready formats. This removes manual handoffs and enforces standards across the full data lifecycle.

    Machine Learning Concept with Fiber Optic Network Connections
    Machine Learning Concept with Fiber Optic Network Connections
  • End-to-End Data Transformation

    Raw data must pass through multiple stages before it is usable for machine learning. Data Canvas provides a unified pipeline for preprocessing, annotation, quality assurance, and post-processing; ensuring that every dataset is consistently transformed, validated, and delivered in model-ready formats. This removes manual handoffs and enforces standards across the full data lifecycle.

    Machine Learning Concept with Fiber Optic Network Connections
  • Enabling Complex Use Cases

    From autonomous driving and medical imaging to natural language processing and video analytics, many advanced AI applications depend on richly annotated data to handle edge cases, rare conditions, and nuanced scenarios. Data Canvas supports these high-complexity workflows by enabling multi-modal annotation, layered labels, and continuous refinement—so models can learn from the full depth and variability of real-world data.

    big data connection technology concept
    big data connection technology concept
  • Enabling Complex Use Cases

    From autonomous driving and medical imaging to natural language processing and video analytics, many advanced AI applications depend on richly annotated data to handle edge cases, rare conditions, and nuanced scenarios. Data Canvas supports these high-complexity workflows by enabling multi-modal annotation, layered labels, and continuous refinement—so models can learn from the full depth and variability of real-world data.

    big data connection technology concept
  • Scalable Collaboration & Automation

    Modern annotation projects involve distributed teams, large data volumes, and evolving requirements. Data Canvas enables role-based collaboration, workflow orchestration, and AI-assisted labeling, allowing organizations to scale annotation programs efficiently while maintaining visibility, control, and performance.

    Abstract image
    Abstract image
  • Scalable Collaboration & Automation

    Modern annotation projects involve distributed teams, large data volumes, and evolving requirements. Data Canvas enables role-based collaboration, workflow orchestration, and AI-assisted labeling, allowing organizations to scale annotation programs efficiently while maintaining visibility, control, and performance.

    Abstract image
  • Regulatory-Ready Data for Auditable AI

    In regulated industries such as healthcare and financial services, training data must be accurate, traceable, and auditable. Data Canvas provides structured workflows, version history, and review trails that support regulatory requirements and defensible AI systems.

    vivid data flow concept background
    vivid data flow concept background
  • Regulatory-Ready Data for Auditable AI

    In regulated industries such as healthcare and financial services, training data must be accurate, traceable, and auditable. Data Canvas provides structured workflows, version history, and review trails that support regulatory requirements and defensible AI systems.

    vivid data flow concept background

Where Data Quality Begins

Where Data Quality Begins

AI Enablement Built for Scale

Trusted by Global Enterprises

Data Canvas provides a structured environment for preparing, validating, and managing data across AI workflows. It standardizes how datasets move through transformation, review, and delivery while maintaining consistency, traceability, and quality at scale.

Data Canvas provides a structured environment for preparing, validating, and managing data across AI workflows. It standardizes how datasets move through transformation, review, and delivery while maintaining consistency, traceability, and quality at scale.

Data Ingestion & Preprocessing

Handles large-scale ingestion and preparation of structured and unstructured data across images, text, video, audio, and documents, with built-in cleaning, normalization, and transformation to prepare inputs for annotation.

Data Ingestion & Preprocessing

Handles large-scale ingestion and preparation of structured and unstructured data across images, text, video, audio, and documents, with built-in cleaning, normalization, and transformation to prepare inputs for annotation.

Data Ingestion & Preprocessing

Handles large-scale ingestion and preparation of structured and unstructured data across images, text, video, audio, and documents, with built-in cleaning, normalization, and transformation to prepare inputs for annotation.

Annotation & Labeling Tooling

Provides tools for bounding boxes, segmentation, keypoints, text, and audio labeling, combining intuitive interfaces with automation and AI-assisted workflows.

Annotation & Labeling Tooling

Provides tools for bounding boxes, segmentation, keypoints, text, and audio labeling, combining intuitive interfaces with automation and AI-assisted workflows.

Annotation & Labeling Tooling

Provides tools for bounding boxes, segmentation, keypoints, text, and audio labeling, combining intuitive interfaces with automation and AI-assisted workflows.

Quality Assurance & Review

Built-in review workflows ensure accuracy and consistency through conflict resolution and consensus mechanisms, supported by automated checks for labeling errors and inconsistencies.

Quality Assurance & Review

Built-in review workflows ensure accuracy and consistency through conflict resolution and consensus mechanisms, supported by automated checks for labeling errors and inconsistencies.

Quality Assurance & Review

Built-in review workflows ensure accuracy and consistency through conflict resolution and consensus mechanisms, supported by automated checks for labeling errors and inconsistencies.

Post-Processing & Enrichment

Transforms and validates labeled data for ML pipelines, enriches datasets with metadata and derived features, and exports outputs in standard formats for training, evaluation, and analytics.

Post-Processing & Enrichment

Transforms and validates labeled data for ML pipelines, enriches datasets with metadata and derived features, and exports outputs in standard formats for training, evaluation, and analytics.

Post-Processing & Enrichment

Transforms and validates labeled data for ML pipelines, enriches datasets with metadata and derived features, and exports outputs in standard formats for training, evaluation, and analytics.

Collaboration & Workflow

Enables role-based access for annotators, reviewers, and project managers, coordinating task assignment, progress tracking, orchestration, and in-platform communication.

Collaboration & Workflow

Enables role-based access for annotators, reviewers, and project managers, coordinating task assignment, progress tracking, orchestration, and in-platform communication.

Collaboration & Workflow

Enables role-based access for annotators, reviewers, and project managers, coordinating task assignment, progress tracking, orchestration, and in-platform communication.

Scalability & Automation

Scales high-volume projects through AI-assisted labeling, semi-automated workflows, and flexible deployment across cloud, hybrid, and on-prem environments.

Scalability & Automation

Scales high-volume projects through AI-assisted labeling, semi-automated workflows, and flexible deployment across cloud, hybrid, and on-prem environments.

Scalability & Automation

Scales high-volume projects through AI-assisted labeling, semi-automated workflows, and flexible deployment across cloud, hybrid, and on-prem environments.

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