Use Cases & Real-World Applications
We support various real-world AI projects by providing custom data services for specific needs
Data Quality
Through human-in-the-loop and automated checks, we verify data integrity, detect policy violations, and ensure brand alignment across large-scale user-generated or AI-generated content. This approach guarantees safe and high-quality datasets for training and deployment.
Verify and score the relevance of reference documents or citations used by AI outputs. Using automated evaluation tools and human judgment, our annotators perform yes/no relevance checks and validation for sources that language models cite or retrieve. This ensures that every reference supports the content, which is crucial for trustworthy AI-generated reports and answers.
Building benchmark test datasets with thorough annotations for model evaluation. We create question-answer pairs with verified answers or summaries with reference summaries, serving as high-quality test sets. Each data point is carefully annotated (e.g., correct answers, factuality notes, or preference ratings), enabling reliable benchmarking of AI models on tasks like QA, summarization, or translation.
We set up continuous monitoring pipelines that track changes in data distribution over time.
When data drift or anomalies are detected, teams receive alerts, enabling proactive retraining or data updates. This helps maintain consistent model performance in dynamic real-world environments.
AI Training Data
Helping align large language models with human preferences and values. We conduct A/B comparison labeling (ranking two model answers), collect ratings on answer quality, and evaluate outputs for safety issues or cultural bias. These human preference datasets guide LLM fine-tuning (such as Reinforcement Learning from Human Feedback) so that models produce more helpful, safe, and culturally appropriate responses.
Providing ongoing human evaluation of AI system outputs to drive improvement. This can include video-based evaluation (for vision-language models or generated videos) and detailed assessments of generative text or audio. We design and execute benchmarking studies where human judges rate AI output quality on various criteria. The feedback helps identify weaknesses and measure progress for iterative model updates.
Generating and refining data in more than 100 languages, including rare languages like Cebuano and Gaelic, to broaden AI capabilities. To augment training data, our linguists create idiomatic expressions, dialogues, and content in target languages. We also enhance existing datasets – for instance, expanding a Gulf Arabic corpus with more dialectal variety or correcting literal translations to more natural phrasing. The result is richer multilingual training data that improves model fluency and cultural relevance across locales.
We generate or augment datasets with realistic, artificially created examples—especially valuable when real-world data is scarce or sensitive. This approach helps AI models handle rare events and maintain performance across underrepresented situations without compromising privacy or coverage.
Applied Solutions
Fine-tuning the retrieval component of RAG (Retrieval-Augmented Generation) pipelines. We assist in assessing and improving the relevance of retrieved documents or sources for questions, extracting key information, and labeling query-document pairs. This human-guided tuning of the retriever boosts the accuracy of RAG systems in applications like enterprise search and question answering.
We remove or mask sensitive information (personally identifiable data, confidential records) to meet strict data protection regulations such as GDPR or HIPAA. Our techniques preserve the utility of datasets while safeguarding privacy, ensuring your AI projects remain both compliant and effective.
We craft domain-appropriate summaries and automated text generation (e.g., legal documents, financial reports, or technical manuals). Our workflows keep the generated content factually accurate and aligned with industry-specific language, style, and compliance requirements.
Our domain experts design customized data workflows—from medical text annotation (e.g., clinical notes, PHI redaction) to legal contract parsing or financial transaction normalization. We address regulatory and industry-specific challenges to deliver reliable, compliant data in specialized fields.
We collect, annotate, and align textual database queries with their corresponding SQL statements across multiple dialects (MySQL, PostgreSQL, SQL Server, etc.). This training data fuels AI models that convert plain-language requests into valid SQL, empowering non-technical users to access insights without writing code.
