Web corpora curated for training, not just collected.
Models are only as good as the data behind them. We build domain and language-specific corpora from public web sources, with provenance recorded, licence signals respected, duplicates removed and quality filters applied, ready for labelling or fine-tuning.
Generic crawl dumps are noisy, repetitive and heavy with boilerplate, spam and harmful content. Teams building models for Arabic, regional commerce or specialist domains struggle to find enough clean, well-documented text and structured examples.
You receive a corpus shaped around your model's purpose, with each document traceable to its source and licence signals. Quality and safety filtering is documented, so your team can explain what went into training and why.
Everything needed to run it in production.
Source curation
We select domains and sources by relevance, language and quality, and record licence and robots signals for each one.
Licence-aware collection
Content with restrictive licence terms or opt-out signals is excluded or flagged, according to the policy you set.
Near-duplicate removal
Exact and near-duplicate documents are removed with hashing and similarity methods, reducing memorisation and wasted compute.
Quality and toxicity filtering
Boilerplate, spam, low-information pages and toxic content are filtered using configurable classifiers and thresholds.
Personal data minimisation
Emails, phone numbers and other identifiers are detected and masked or removed before delivery.
Labelling-ready formats
Output in JSONL, Parquet or instruction-pair formats with metadata fields your labelling tools and training pipelines can ingest directly.
What lands in your systems.
| doc_id | source_domain | language | licence_signal | tokens | quality_score | toxicity_score | status |
|---|---|---|---|---|---|---|---|
| d_0019a4 | news-site-a.example | ar | open, attribution | 1,284 | 0.91 | 0.02 | kept |
| d_0019b7 | gov-portal-b.example | en | public sector licence | 3,906 | 0.88 | 0.01 | kept |
| d_0019c2 | forum-c.example | ms | unspecified | 412 | 0.37 | 0.04 | dropped_low_quality |
| d_0019d0 | retail-d.example | pt-BR | terms reviewed | 655 | 0.79 | 0.01 | dropped_near_duplicate |
| d_0019e8 | blog-e.example | es-MX | opt-out signal | 1,120 | 0.84 | 0.03 | excluded_licence |
Illustrative rows. Your schema, field names and formats are agreed during scoping.
Who uses it, and for what.
Machine learning teams fine-tune language models on a clean Arabic or multilingual corpus. Documented filtering makes evaluation results easier to interpret.
Retail AI teams train product classifiers on catalogue text and attributes from many marketplaces. Consistent taxonomy mapping speeds up labelling.
Content and search teams build domain-specific retrieval sets for assistants and search. Provenance fields allow answers to link back to their sources.
Risk and compliance teams require evidence of how training data was sourced. The manifest records licence signals and exclusions for governance review.
- Scope. Tell us the sources, fields and frequency. We confirm feasibility within a day.
- Free sample. A real sample from your own target source, in your format.
- Build. Engineers build extractors tuned to each source. No generic templates.
- Validate. Automated and manual QA on every run before anything ships.
- Deliver and monitor. Scheduled delivery, monitored pipelines, fast fixes when sites change.
Related services
Industries that use it
How do you handle licensing and opt-outs?
We record licence and robots signals for each source and apply the policy you choose, such as excluding content with opt-out signals or restrictive terms. The manifest lists every source and its status for your legal review.
How do you remove duplicates?
We remove exact duplicates by hashing and near-duplicates using similarity clustering at document and paragraph level. Each kept document carries a cluster ID so you can inspect what was removed.
Can you filter toxic or harmful content?
Yes. Configurable classifiers score each document for toxicity and quality, and you set the thresholds. Filtered samples are available for review so you can tune the settings.
What formats do you deliver?
Typically JSONL or Parquet with text and metadata fields, or instruction-response pairs for fine-tuning. We can match the schema your labelling or training pipeline expects.