1. Brief
Define the dataset requirements, contributor profile, capture conditions, annotation spec, quality thresholds, volume, and output format.
Collect new data, source raw datasets, or annotate data you already own. Build structured datasets for model training, search relevance, recommendation systems, and LLM evaluation.
Collect net-new image, video, audio, and text data against your task spec. Define the geography, contributor profile, device requirements, environment, volume, and output format. Receive data structured to your schema and ready for annotation, training, or evaluation.
First-person image and video captured from wearable, mobile, or mounted devices. Build datasets for robotics, computer vision, navigation, activity recognition, and human-object interaction across defined environments, actions, and contributor profiles.
Start with data that already exists. Use raw, licensed, off-the-shelf, or client-owned datasets as your base. We clean, filter, deduplicate, structure, and enrich them against your model requirements, schema, and quality thresholds, ready for your training or evaluation workflow.
Collect net-new image, video, audio, and text data against your task spec. Define the geography, contributor profile, device requirements, environment, volume, and output format. Receive data structured to your schema and ready for annotation, training, or evaluation.
Side-by-side ranking, NDCG relevancy, and query intent tagging. Contributors evaluate result quality against your defined relevance criteria, capturing the judgments that train and benchmark ranking models. Built for search, recommendation, and retrieval systems.
Preference ranking, safety evaluation, and tone and accuracy grading for LLMs. We capture human judgment on model outputs against your rubric, the feedback signal that shapes model behavior. Scales from small preference sets to large ongoing alignment pipelines.
Fully managed, from data spec to delivery
Define the dataset requirements, contributor profile, capture conditions, annotation rubric, quality thresholds, and output format. Acquirox handles collection, preparation, labeling, validation, QA, and delivery. Your team scopes the project once and receives structured, model-ready data.
Works with your existing stack
Use your current storage, labeling environment, and delivery workflow. Acquirox supports Label Studio, V7, CVAT, and proprietary annotation environments, with output in JSON, CSV, COCO, XML, or Parquet. No platform migration required.
Verified contributor network
Every collection submission and annotation task is completed by a verified contributor. Hardware fingerprinting, task-level checks, contributor performance history, and QA controls reduce bot activity, duplicate submissions, and inconsistent output.
Elastic collection and labeling capacity
Adjust volume as dataset requirements change. Run a targeted collection project, an ongoing annotation pipeline, or bursts of up to 1M+ tasks without building a fixed workforce or restarting contributor recruitment.
From first brief to model-ready data in three steps – validated on a pilot before full delivery.
Acquirox handles data collection, dataset preparation, labeling, QA, and delivery - so your engineers stay focused on model development.
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