Guides
Data work, run like production.
Seven guides, one lens: a model does the semantic work, a server keeps the state, and every accepted value has an acceptance record behind it. They stand on their own — read them with or without the product.
AI data pipeline: what it is and how to build one that survives the session
The five stages, the state question that separates pipelines from demos, and a seven-box checklist you can argue with.
Read the guide → Choosing toolsData extraction tools in 2026: parsers, models, and the accounting in between
Four tool families sorted by input, a decision path, and the acceptance accounting every comparison skips.
Read the guide → Core mechanismLLM data extraction: batches, gates, and why attempt #2 exists
Four failure modes, pull-based batches sized to judgement, the golden gate's three modes, and the ledger where retries are instruments.
Read the guide → The contractStructured data extraction with LLMs: schema first, then scale
Valid JSON is not valid data. What constraint tooling guarantees, what it can't, and the declared checks that judge every submission.
Read the guide → Growing columnsData enrichment: growing the columns your data never had
Lookup versus judgement, the 2026 tool landscape sorted honestly, and why enriched is not the same as trusted.
Read the guide → The clean edgeAI data cleaning: mechanical rules first, model judgement second
Where rules end and models begin, five patterns for a model in the loop, and the account every rejected row deserves.
Read the guide → CollectionAI web scraping: from brittle selectors to agent-run collection
Three modes wearing one name, where dumb selectors still win, and why most scraped datasets die in stage two.
Read the guide → The full argumentHow Tablize works — the mechanism, argued in full
Two dead ends, four steps, three gate modes, and the honest boundary of what a landed row does and does not mean.
Read the argument →Public beta · free · macOS, Linux and Windows