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About UsWho we are and what we do.AI Training Data Built for Your Model, Not the Market
Off-the-shelf datasets rarely fit a production model. We source, label, and QA text, image, audio, video, and multimodal data to your own schema, at the scale your project needs.
1,000 of 240,0000.960.940.980.921,000 of 86,4000.950.930.970.911,000 of 12,700 clips0.970.950.930.961,000 of 4,300 clips0.940.920.960.931,000 of 58,000 pairs0.980.950.910.94Data for Every Stage, From Pre-Training to Evaluation
A model needs different data at every stage. We scope each dataset to the stage it serves, so you are not fine-tuning on corpora built for pre-training.
Pre-training corpora
Large-scale, deduplicated, filtered text and multimodal corpora with documented provenance.
Fine-tuning & SFT sets
Instruction, task, and domain data written or labeled to the behavior you want the model to learn.
Preference & RLHF data
Ranked responses, side-by-side comparisons, and rationales from reviewers who know the domain.
Evaluation & red-teaming
Held-out benchmark sets and adversarial prompts to measure the model before your users do.
Five Modalities, One Delivery Standard
Whatever the modality, you get the same schema discipline, the same QA tiers, and the same provenance documentation.
Text
Documents, conversations, reviews, and long-form web content.
Images
Product shots, shelf photos, documents, and scene imagery.
Audio
Calls, voice queries, and field recordings across languages.
Video
Store, site, and process footage for temporal models.
Multimodal
Paired records where one signal explains another.
Quality Control on Every Batch
Bad labels are more expensive than missing data. Every batch passes automated validation and human review before it reaches your bucket.
Schema validation first
Records that do not match your schema never reach a human reviewer, or your bucket.
Multi-tier human review
A second reviewer checks a defined share of every batch, and senior reviewers settle any conflicts.
Gold-set benchmarking
Each batch is scored against a labeled benchmark so drift shows up as a number, not a surprise.
Bias & coverage checks
Class balance, language, and demographic coverage reviewed against the distribution you specified.
Full audit trail
Every record carries annotator tier, review history, and timestamps for reproducibility.
Rework, not excuses
Anything below the agreed accuracy threshold is re-annotated at our cost before delivery.
From Scope to Delivery in Five Steps
See real labeled records before you commit to volume.
Scope
Define modality, volume, domain, schema, and format together.
Source
Collect or license the raw data your model actually needs.
Label
Domain-matched annotators structure the data to your schema.
Validate
Multi-tier QA checks accuracy and coverage before delivery.
Deliver
Clean, ready-to-train datasets in your format, on your timeline.
Everything a Production Dataset Needs
Sourcing, labeling, licensing, and quality control are one engagement, not four vendors to coordinate.
Sourcing & collection
Data sourced or collected to your project scope, from public web, licensed sources, or your own systems.
Labeling & annotation
Structured annotation to your schema, reviewed for consistency across annotators and batches.
Licensing support
Third-party datasets cleared for commercial use, with the paperwork handled before delivery.
Quality control
Automated validation and multi-tier human review, with a QA report on every batch.
Annotation Built Around Your Label Schema
Guidelines are written with your team, calibrated on a gold set, and versioned. When the taxonomy changes, the change is documented and re-applied, not improvised.
Guidelines written with your team
Edge cases documented up front, so annotators are not guessing at your intent.
Calibrated on a gold set
Annotators qualify against a labeled benchmark before touching production data.
Agreement scored per batch
Inter-annotator agreement is reported with every delivery, not summarized at the end.
Versioned taxonomies
A schema change is a new version, with the affected records re-labeled and traceable.
Need a field that is not listed? Define it once and it is in every record.
Who Uses Our AI Training Data
AI & ML teams
Get training-ready data to your schema instead of spending sprints on collection and cleanup.
Data engineers
Receive validated batches in your format, so ingestion is a load job, not a parsing project.
Applied research teams
Commission evaluation and preference data that targets the exact behavior you are measuring.
Product & AI leaders
Move from model idea to labeled dataset with one accountable vendor and a written scope.
Why Teams Choose APISCRAPY for Training Data
Built With Compliance in Mind
Every dataset ships with documented provenance, so your legal and model-risk teams can sign off without a discovery exercise.
Sourced and collected within the data protection regulations relevant to your industry.
Provenance and licensing records for every dataset, included with delivery.
ISO 27001:2022 and ISO 9001 certified, CMMI Level 3 appraised, and GDPR compliant.
Encrypted in transit and at rest, with role-based access, NDAs, and full audit logs.
Delivered in the Format Your Pipeline Already Reads
Datasets land in your own storage, in your own format, with a manifest, a data card, and provenance documentation alongside them.
Formats your tooling reads
JSONL, Parquet, CSV, COCO, YOLO, WebDataset, or a layout you define.
Your storage, your account
Delivered to S3, GCS, Azure Blob, SFTP, or a private Hugging Face repo.
Manifest and data card
Every batch ships with counts, class balance, QA scores, and a provenance record.
Refresh on a schedule
One-off delivery, or recurring batches that keep the training set current.
{
"id": "rec_8f21c4",
"modality": "text",
"input": "my order shipped to the old address",
"labels": {
"intent": "delivery_issue",
"sentiment": "negative",
"entities": [{"type": "address", "span": [26, 37]}]
},
"annotator": { "tier": "senior", "domain": "retail_support" },
"qa": { "passes": 2, "agreement": 0.96, "reviewed_at": "2026-07-30" },
"license": "client_owned"
}AI Training Data Questions
What is AI training data?
AI training data is the labeled examples a machine learning model learns from, such as text tagged by intent, images with bounding boxes, or audio with transcripts. Its quality sets the ceiling on model accuracy, so it has to match your task, your label schema, and the real-world cases your model will see.
How is your AI training data sourced?
A combination of licensed datasets, direct collection from public sources, and data you supply, matched to project scope and the compliance requirements of your industry.
Can I buy ready-made datasets, or only custom builds?
Both. Off-the-shelf sets exist for common use cases, and most enterprise projects end up as a custom build scoped to a specific model and label schema.
Do I own the data you deliver?
Yes, for everything we create for you. Labels, annotations, and custom datasets transfer to you on delivery, with IP transfer written into the contract and no ongoing fees. Where a dataset includes licensed third-party data, you receive a license cleared for your use, documented in the delivery.
What quality checks does the data go through?
Automated validation against your schema, then multi-tier human review by domain-matched annotators, with agreement scores and a gold-set benchmark reported per batch.
What data types do you support?
Text, image, audio, video, and multimodal datasets, including paired records such as image plus specification or video plus transcript.
How is training data priced?
Scoped per project on modality, volume, label complexity, and QA tier. You get a written quote after a short scoping call, and a pilot batch before committing to volume.
How long does a first delivery take?
A pilot batch is typically ready within days of guideline sign-off. Full delivery depends on volume and modality, and is committed to a written schedule before work starts.
Can you keep a dataset refreshed over time?
Yes. Many clients run an ongoing pipeline with scheduled refresh batches, so the training set keeps pace with the real-world distribution the model sees.