Enhance model performance by going deeper into situational specifics. We extract data’s true meaning to enable more insightful outputs. Our AI data solution ensures contextual wealth across stages of the data pipeline—sourcing, annotation, deployment, and evaluation, resulting in AI systems that are performant, trustworthy, inclusive, and grounded in reality.
A multi-layer framework
for contextual fidelity
A multi-layer framework
for contextual fidelity
Retain the richness of cultural overtones and relevance of real-world context. Our data collection for AI prioritizes diversity, reaching into identified and untapped sources to ensure contextual fidelity from the first point of entry.
Capture social cues, situational subtleties, natural interaction patterns, and edge cases through supervised, in-person collection at selected locations.
Gather data from real-world environments that retains user behavior, actual sequence of events, uniqueness of phrasing or words, and their intent.
Pursue AI in niche domains with expert curated and verified datasets to fill gaps of high-quality and style-specific examples for model training.
Produce synthetic data strategically, guided by experts, to capture edge cases and culturally specific scenarios beyond existing protocols.
Turn raw and unfiltered data into context-rich datasets with precise annotation by qualified domain experts and consistent feedback loops.
Leverage diversity to bring out data's inherent value, be it from a single source or combined modalities. Our data annotation service is supported by annotators working asynchronously with streamlined, intuitive labeling tools to ensure accuracy.
Supported modalities: Image, Text, Video, Speech, Audio, Digital Interface
Annotation types: Bounding box, Polygon, Segmentation, Classification, Transcription, Entity recognition
Progress model’s learning capabilities with human expertise and machine intelligence:
Boost the dependability of AI with our comprehensive QA approach:
Stay on top of progress with tasks monitoring to swiftly spot and resolve bottlenecks through smart redistribution. At the same time, track annotation performance with dashboard insights on total output, precision rates, and error types, ensuring consistent quality against expert-defined benchmarks and our 98% accuracy standard.
Accelerate AI implementations with off-the-shelf datasets curated and vetted by industry experts. Designed as a solid foundation, these datasets let you train and fine-tune models for complex tasks like reasoning, computation, and logic. Spanning multiple domains, our growing library supports faster, more efficient AI adoption.
| DOMAIN | DATASET ID | USE CASE |
|---|---|---|
| Language | indonlu-eval-sealionv3-vs-sahabat-ai-v1-round2 | Performance evaluation of two models for tasks in Bahasa Indonesia, tested with over 50 challenges in language, domain knowledge, geography, and combination tasks. |
| Recruitment | Reasoning_Patterns_AI_Hiring_Bias_SEA | Hiring simulations across six AI models to capture decisions, confidence levels, tone shifts, and bias signals. |
| Coding | advent_of_code_ecv_dataset | Improve contextual understanding of coding datasets by comparing the results of various approaches in coding challenges. |
| Education | STEM-en-ms | Bilingual dataset with tasks evaluating reasoning skills in Science, Technology, Engineering, and Mathematics (STEM) subjects. |
Assemble reliable data by combining human experience, real-world insights, and robust systems, to stay aligned with your industry’s trajectory.
Advance AI with human insights as talents from diverse disciplines, selected through domain-specific assessments validate data for accuracy and relevance.
Safeguard data with supervised data collection, precise transformation, and controlled output processes that meet high-level security and encryption requirements.
Ensure contextual quality in data by benchmarking against expert-vetted datasets and leveraging a dedicated model evaluation tool tailored for industry-grade AI systems.
Power AI models with datasets curated with contextual fidelity for grounded decision-making.
