This blog explores how to build robust safety evaluation pipelines for Gen AI. Examines the key dimensions of safety,...
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Ensure your Gen AI models are accurate, fair, safe, and production-ready, through expert human validation
Human Intelligence Powering Responsible AI
Digital Divide Data (DDD) is a global data solutions partner helping organizations build, evaluate, and scale AI systems responsibly. We combine deep domain expertise, structured evaluation frameworks, and a highly trained global workforce to deliver reliable, unbiased, and high-quality AI outcomes at scale.
Data Types We Cover
Text:
Evaluate NLP, reasoning, summarization, generation, and conversational accuracy.
Image:
Assess visual understanding, classification, detection, captioning, and reasoning accuracy.
Video:
Test temporal understanding, action recognition, scene interpretation, and event consistency.
Audio:
Assess speech recognition, transcription quality, sentiment, and audio understanding.
Sensor:
Test temporal understanding, action recognition, scene interpretation, and event consistency.
Our Model Evaluation Solutions
Accuracy Testing
Measure how correct the model’s outputs are compared to ground truth (e.g., correct answers, facts, logical reasoning).
Factual Consistency Evaluation
Test whether the model generates factually accurate information and reduces hallucinations.
Bias and Fairness Assessment
Verify if the model’s outputs exhibit bias based on factors such as gender, race, culture, geography, etc.
Toxicity and Safety Testing:
Evaluate if the model produces harmful, offensive, or dangerous content.
Multilingual and Localization Testing
Test model performance across different languages, dialects, and cultural contexts.
Response Relevance and Context Awareness
Evaluate whether the model’s answers stay on-topic, logical, and appropriate to the conversation or input.
Task-Specific Evaluation
Measure model performance on specialized tasks like code generation, summarization, translation, image captioning, etc.
User Preference and Satisfaction Testing
Collect human feedback (e.g., ranking outputs) to see if users find the model’s responses helpful and high quality.
Fully Managed, End-to-End Model Evaluation Workflow
From one-time evaluations to continuous, always-on pipelines, DDD manages the complete model evaluation lifecycle
Why Choose DDD?
Human-in-the-loop Evaluation
We combine structured evaluation methodologies with trained human evaluators who understand nuances, context, and domain complexity, which is critical for assessing reasoning, bias, and alignment.
Multilingual Expertise
Our distributed workforce enables multilingual and localization testing across languages, dialects, and regions, ensuring your model performs reliably in global markets.
Always-On Evaluation Readiness
Whether you need a one-time audit or continuous evaluation pipelines, DDD scales with your model lifecycle, from pre-deployment validation to post-launch monitoring.
Flexible
Platform-agnostic by design. We integrate with your tools, workflows, and infrastructure, never forcing proprietary systems.
What Our Clients Say
DDD’s Commitment to Security & Compliance
Your data is protected at every stage through rigorous global standards and secure operational infrastructure:Your sensitive data is protected at every stage through rigorous global standards and secure operational infrastructure.
SOC 2 Type 2
Verified controls across security, confidentiality, and system reliability
ISO 27001
Holistic information security management with continuous audits
GDPR & HIPAA Compliance
Responsible handling of personal and medical data
TISAX Alignment
Automotive-grade protection for mobility and vehicle-AI workflows
Blogs
Deep dive into the latest technologies and methodologies that are shaping the future of Gen AI.
Evaluating Gen AI Models for Accuracy, Safety, and Fairness
This blog explores a comprehensive framework for evaluating generative AI models by focusing on three critical dimensions: accuracy, safety,...
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The Art of Data Annotation in Machine Learning
Data Annotation has become a cornerstone in the development of AI and ML models. In this blog, we will...
Read MoreResponsible AI Starts with Rigorous Evaluation
Frequently Asked Questions
What is fine-tuning, and why does it matter for GenAI models?
DDD evaluates both enterprise models (custom LLMs, copilots, decision systems) and foundation models across text, image, audio, video, sensor, and multimodal use cases.
How is DDD’s model evaluation different from automated benchmarking?
Automated benchmarks miss nuance. DDD combines human expert evaluation with structured frameworks to assess reasoning quality, bias, safety, context awareness, and real-world behavior that automated metrics alone cannot capture.
Can you evaluate models before and after deployment?
Yes. We support pre-deployment validation, post-deployment audits, and continuous evaluation pipelines to monitor model performance as data, prompts, and use cases evolve.
Do you support multilingual and global evaluations?
Absolutely. DDD conducts evaluations across multiple languages, dialects, and cultural contexts, helping ensure your model performs consistently and appropriately in global markets.
How do you measure bias and fairness?
We design targeted test cases and scenarios to identify bias related to gender, race, culture, geography, and socio-economic context, and provide actionable insights to reduce risk and improve alignment.
How is human feedback collected and validated?
Our trained evaluators follow standardized guidelines, scoring rubrics, and quality checks, with multi-layer reviews to ensure consistency, reliability, and high-signal feedback.