Ingest raw web data at scale,formatted for AI models.
Automatically crawl, parse, deduplicate, and vectorize web targets into pristine training datasets. Direct pipelines to Hugging Face, Pinecone, and Parquet.
Ingestion Orchestrator
Control target scraping speeds, re-route proxy region configurations, and preview raw-to-JSON extractions.
Systems load
Live Parser Parser
<body><h2>Trending repositories</h2><div class='repo'>react - Declarative UI library (240k stars)</div></body>{
"source": "github.com",
"title": "Trending repositories",
"items": [
{"name": "react", "stars": 240000}
],
"tokens": 481
}Designed for AI Engineering
A comprehensive pipeline stack for data vectorization, schema validation, and strict compliancy scraping.
Interactive Schema Modeler
Specify and parse custom database parameters on the fly. Click fields below to customize the JSON schema output dynamically.
{
"dataset_schema": {
"source": "url_string",
"title": "string_parsed",
"content": "markdown_clean",
"vector_embedding": "float32[1536]"
}
}Semantic Vector Chunking
Slices parsed raw structures into text nodes, runs embed algorithms, and deposits vectors dynamically.
LSH Deduplication
Removes repetitive banners, duplicate site footers, and template noise, preserving raw content data.
PII Scrubber & Audit Log
Enforces robots.txt guidelines and automatically scrubs emails, phone numbers, and location values before compilation.
Curated Dataset Gallery
Browse structured datasets ready for download, fine-tuning, and vector mapping.
Reddit Dialogue & Opinion Graph
28 Billion tokensAnonymized conversational trees from top informational subreddits, clean of bot spam, structured for training LLM conversational agents.
Facebook Public Page Marketing Corpora
14 Billion tokensExtracted post data from verified corporate and news public pages. Optimized for marketing copywriting and brand sentiment models.
AI Academic Papers Corpus
42 Billion tokensPruned academic PDFs parsed into structured markdown blocks containing titles, abstracts, and reference annotations.
Reddit Dialogue & Opinion Graph
Anonymized conversational trees from top informational subreddits, clean of bot spam, structured for training LLM conversational agents.
{
"subreddit": "r/machinelearning",
"thread_id": "t3_1dx492a",
"discussion_text": "Evaluating local model performance on complex logic...",
"replies": [
{"author_karma": 12800, "body": "Llama 3 8B scales well if fine-tuned on clean datasets..."}
],
"upvotes": 412
}Powering Boutique AI Labs
Meet the specialized, niche AI research teams and startups training their models on reworkd structured datasets.
DeepSoil AI
Trains local soil humidity and crop-yield prediction models on localized agricultural web records and historical weather datasets.
Lexicon Legal Tech
Refines legal text summarization and contract compliance algorithms using structured public court transcripts and legal filing archives.
Aetheris Research
Develops niche ecological simulation systems using environmental web archives, monitoring global sensor registers.
VoxelCraft AI
Trains game texture synthesis and text-to-asset models using parsed public catalog metadata and image alignments.
OmniScribe NLP
Trains medical conversation transcription systems on specialized regional clinic publications and public files.
Verba Translation
Fine-tunes minority language translation engines on regional conversational and public dialogue logs.
Raw-To-Train Pipeline
Six distinct pipeline stages that sanitize, vectorise, and sharded web nodes for model pretraining.
URL Discovery
Smart crawlers map targets, parse domain sitemaps, resolve CAPTCHAs, and index outbound links into the queues dynamically.
DOM Extraction
Strips layouts of CSS stylesheets, tracking pixels, scripts, and sidebar links, extracting clean semantic Markdown.
PII Clean-Up
An automated regex and NLP sanitization loop scrubs emails, phone numbers, addresses, and handles robots.txt compliance.
LSH Deduplication
Executes MinHash LSH checks at scale, filtering repetitive paragraphs and duplicate files to prevent LLM training overfit.
Vector Chunking
Tokenizes string text by Llama or OpenAI token indexes, segments into vectors, and generates float32 database embeddings.
Parquet Export
Compresses processed tokens into sharded Parquet files and pushes datasets directly to Hugging Face or AWS S3 pools.
Aligned for Generative Training.
Reworkd datasets compile and map web text structures directly into the exact instruction wrappers required by state-of-the-art models. Skip formatting scripts and train instantly.
Datasets are mapped to raw instruction-tuning wrappers (ChatML tags) to facilitate pre-packaged SFT (Supervised Fine-Tuning) paths directly.
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant.
<|start_header_id|>user<|end_header_id|>
{{instruction_crawled_data}}
<|start_header_id|>assistant<|end_header_id|>