Speech, image, video, sensor and text - collected, labeled and validated by a global human workforce, until it's structured enough for a model to learn from.
Every model begins as unlabeled noise. Fiwiks exists in the space between that noise and a dataset a model can trust — collecting, annotating, validating and operating the workforce behind it all.
Whatever a model needs to hear, see or sense, we can source it at scale - read speech to satellite imagery, motion capture to millimeter level sensor streams.
Scripted, spontaneous and conversational speech across languages, accents and age groups, plus wake-word and IVR corpora.
Photography sourced or captured to spec across everyday, clinical and industrial settings.
Footage that captures behavior over time. How people move, drive, shop and work.
Depth, pose and interaction data for embodied and immersive systems.
Streams from the physical world - position, motion and environment, captured continuously.
Written corpora built for retrieval, comprehension and conversation.
Every box, mask, transcript and preference ranking is produced against a spec, and checked against one annotation is only useful if it's consistent.
Pixel and region-level labels for detection, segmentation and tracking models.
Temporal labeling for perception systems that need to understand sequences, not single frames.
Turning sound into structured, searchable, speaker-attributed text.
Structure layered onto language, for search, moderation and understanding tasks.
Human judgment layered onto model output, where the label is an opinion that has to be earned.
A dataset is only as good as its worst undetected error — so every batch is re-examined.
Beyond datasets: human evaluation of model output, and the engineering and applied-AI work that turns a dataset into a deployed system.
Human review of what a model produces, not just what it's trained on.
Custom model development and the data infrastructure that keeps it fed.
Data operations at scale need a hardened perimeter and cloud that scales with them, so we run both as standing capabilities, not add-ons.
Offensive testing, monitoring and response for teams that can't afford to find out the hard way.
The infrastructure layer underneath every dataset, pipeline and deployed model.
AI data work is a human coordination problem before it's a technical one. We recruit, train, schedule and pay a distributed contributor network, and manage the language expertise and quality monitoring that keeps it consistent.
Most vendors do one stage. Sourcing, labeling, validating, evaluating and operating the workforce behind it is one continuous job we treat it that way.
Distributed talent across languages and geographies, coordinated as one workforce.
Every batch is validated against a gold standard before it ships.
Data moves through hardened infrastructure, monitored end to end.
Projects sized for a pilot or a production pipeline, without a re-architecture.
Fixed scope, ongoing operations, or embedded team structured around the work.
Custom datasets built and delivered on a schedule that matches your training runs.
Every engagement starts with a scoping conversation modality, volume, quality bar, timeline.