9 specialized AI agents tap into 290 million+ scholarly works, scrape the web, or synthesize from scratch — creating, validating, and exporting research-grade training data at scale.
290M+
Scholarly Works
9
Specialized Agents
6
Sourcing Modes
10+
LLMs for Diversity
Our Scholarly sourcing mode draws from one of the world's largest academic databases — 290 million+ peer-reviewed papers, patents, and publications across medicine, law, engineering, biology, physics, and every academic discipline. Generate research-grade datasets backed by real science.
290M+
Scholarly Works
All
Disciplines
Each agent specializes in a different stage of the data pipeline. Together, they produce datasets that rival hand-curated, research-grade collections.
Discovery
Crawls the web, scrapes content, and discovers high-quality sources relevant to your topic with intelligent scoring and filtering.
Scoring
Scores every source on quality, relevance, and factual density. Low-quality sources are automatically filtered out.
Documents
Processes your uploaded documents — PDFs, docs, spreadsheets — extracting structured content for the pipeline.
Extraction
Extracts structured facts, claims, and relationships from all sources. Every generated sample traces back to a verified fact.
Synthesis
Creates synthetic data without web scraping — generating novel scenarios, edge cases, and domain-specific reasoning chains.
Generation
Generates diverse Q&A pairs, instructions, and dialogues using multiple LLMs in parallel for maximum diversity and coverage.
Validation
Fact-checks every generated sample against source material. Flags hallucinations, scores accuracy, and suggests corrections.
Optimization
Autonomously adjusts prompts, strategies, and model selection to maximize dataset quality across iterations.
Learning
Learns from mistakes across runs. Failed patterns become permanent avoidance rules, improving every future dataset.
Every dataset flows through three validated stages. No sample enters your dataset without being fact-checked.
Researcher and Analyst discover high-quality sources. Fact Extractor pulls structured claims, relationships, and data points.
Researcher → Analyst → Fact Extractor
Teacher distributes generation across 10+ LLMs in parallel — round-robin, parallel, or all-models mode for maximum diversity.
Hypothesis Generator → Teacher
Critic fact-checks every sample against sources. Meta-Architect tunes strategy. Meta-Learner records patterns for future runs.
Critic → Meta-Architect → Meta-Learner
Choose how your data is sourced. From private documents to scholarly papers to pure synthetic generation — every mode produces research-grade output.
Combines web scraping with your uploaded files for maximum coverage. Best for most use cases.
Extract training data exclusively from your proprietary documents. Nothing leaves your data boundary.
Pure web research — discovers, scores, and extracts from the best online sources for your topic.
Sources exclusively from 290 million+ scholarly works — peer-reviewed papers, patents, and academic publications across every discipline.
Generates data entirely from LLM reasoning — no web scraping needed. Perfect for novel domains and edge cases.
Synthetic generation validated against web sources. Combines creative diversity with factual accuracy.
Every design decision optimizes for accuracy, diversity, and traceability.
Every sample flows through Source → Fact Extraction → Generation → Validation. No shortcuts, no hallucinations.
Teacher agent distributes generation across DeepSeek, Qwen, Claude, Llama, and more — preventing single-model bias.
The Critic agent validates every sample against source material with quality scores, flagging issues before they enter your dataset.
Every Q&A pair links back to the exact source and extracted fact it was generated from. Full audit trail.
Export in JSONL, Parquet, or CSV. Compatible with Hugging Face, OpenAI fine-tuning, and any ML framework.
Q&A pairs, instruction-following, chain-of-thought, code, multi-turn dialogues, classification, summarization, and extraction.
Generate medical Q&A from scholarly papers with validated clinical accuracy
Extract contract clauses, regulatory Q&A, and legal reasoning chains
Code generation pairs, debugging scenarios, and architecture decisions
Financial analysis, market reasoning, and quantitative Q&A datasets
Course material, assessment questions, and adaptive learning datasets
Upload your proprietary documents — create domain-specific datasets for any niche
Describe a topic. 9 agents handle the rest. Export in minutes.