Train smarter AI with richer datasets, starting at data collection
Gather high-quality, multi-source data – from text and speech to sensors and human interactions – to curate datasets that enable hyper-focused model training.
On-the-ground collection
Capture social cues, situational subtleties, natural interaction patterns, and edge cases through supervised, in-person collection at selected locations.
Train smarter AI with richer datasets, starting at data collection
Gather high-quality, multi-source data – from text and speech to sensors and human interactions – to curate datasets that enable hyper-focused model training.
On-the-ground collection
Capture social cues, situational subtleties, natural interaction patterns, and edge cases through supervised, in-person collection at selected locations.
Natural
collection
Gather data from real-world environments that retains user behavior, actual sequence of events, uniqueness of phrasing or words, and their intent.
Expert-generated
data
Pursue AI in niche domains with expert curated and verified datasets to fill gaps of high-quality and style-specific examples for model training.
Synthetic
generation
Produce synthetic data strategically, guided by experts, to capture edge cases and culturally specific scenarios beyond existing protocols.
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
In-depth data collection designed for real-world performance
Capture data grounded in reality and not assumptions, in ensuring model integrity, contextual accuracy, and readiness for deployment.
Field-expert guidance
Data collection exercises are structured with the input of field specialists, and collection exercises are carried out with expert supervision and training (where needed).
Context-forward approach
We design collection methods to prioritize situational nuance, cultural signals, and environmental cues to capture the contextual relevance AI models need to perform in real-world settings.
Scalable and deployment-ready
Our infrastructure supports high-throughput, multi-market data generation without sacrificing quality, enabling a rapid move from sourcing to production.
Coordinate and collect data that matters
Establish quality training data for AI models with custom-designed data collection exercises.
