Source & broker
Licensed text, speech, image, and video; third-party catalogs; first-party exports; synthetic augmentation where it helps, scoped to your domain, locales, and volume targets.
Hardware Form / AI + systems / Training data
Data brokerage · pipelines · compliance
AI training data
Benchmark scores converge. Shippable products still depend on training data that has clear rights, clean labels, and coverage that matches how customers use the model.
Training data collection and brokerage starts with a spec for what the model must see in production. You acquire permissioned corpora or capture on your hardware, sanitize and label to an agreed bar, then deliver versioned datasets with provenance your legal and ML teams can audit.
Training data by sector
HEALTHCAREClinical & imaging AIHIPAA-aware de-ID, clinician labels, device-linked IoMT capture.ROBOTICSEmbodied & manipulationTeleop, egocentric video, sim-to-real metadata, fleet logs.MANUFACTURINGVision & qualityDefect AOI, line-side capture, hard-negative mining.SPEECHVoice & conversationalLocale matrices, consent, device-matched acoustics.AUTONOMYADAS & perceptionMultisensor 3D labels, ODD coverage, privacy sanitize.OTHERCustom sector programRetail, energy, agriculture: scoped pilots with acceptance criteria.
How enterprise data programs are structured now
Most teams run parallel tracks: learning data for fine-tuning, grounding corpora for RAG and agents, and evaluation sets for retrieval quality, hallucination, and policy fit. Buyers and regulators ask for provenance, license scope for training use, and how PII was handled. Permissioned datasets with documentation replace scraped web dumps that create IP and privacy risk.
Pipeline setup
Schemas, storage, batching, and delivery into your training or labeling stack: cloud buckets, Databricks, or on-prem/VPC when data cannot leave your boundary.
Sanitize & normalize
Deduplication, format normalization, PII redaction, policy filters, and metadata enrichment for machine-ready files.
Label & QA
Guidelines, gold sets, inter-annotator agreement targets, and acceptance thresholds for vision, speech, RLHF, and RAG relevance judgments.
Provenance & compliance
Lineage documentation, consent and license summaries, retention rules, and packaging aligned with GDPR, HIPAA, and EU AI Act expectations.
Device-linked capture
Collection on cameras, mics, and sensors from hardware we engineer so field data matches production conditions.
AI training data FAQ
What is AI training data collection?
You source or record data, clean and label it, document rights, and ship versioned datasets for training, fine-tuning, RAG, or evaluation.
Why choose sector-specific programs?
Healthcare, robotics, and autonomy use different compliance rules, label semantics, and failure costs. Sector pages spell out data types and QA patterns that generalist vendors often skip.
How do pilots work?
Fixed scope, acceptance criteria, and a defect taxonomy on a small batch before scale, so quality and legal posture are proven before volume pricing.
Pilots first.
Scale when quality holds.
We agree acceptance criteria before pricing: sample audits, defect classes, remediation policy, and dataset versioning. Production volumes follow the same spec so label semantics stay stable under deadline pressure.