Most extraction tools read text. Alembic sees the whole page — layout, tables, context, meaning. Here’s the architecture that makes document extraction actually work at scale.
Every team extracting data from documents lands on one of three paths. Each one works just well enough to seem promising — and just poorly enough to create real problems downstream.
OCR tools convert pages to raw character strings — then throw away everything that made the document make sense. That table with merged cells, that handwritten annotation, that logo distinguishing an amendment from the original? Gone. You get text. You lose the document.
Large language models are genuinely impressive at reading documents. The problem isn’t capability — it’s reliability. One prompt, one model, no validation, no memory. It works great on the demo. It hallucinates on page 47 of a real contract, and nobody catches it until the data’s already in your system.
Enterprise platforms can handle complexity — after weeks of template configuration, months of training data, and a team dedicated to maintaining the rules. They’re built for organizations with dedicated ops staff and seven-figure volumes. For everyone else, the implementation cost outweighs the extraction value.
Alembic combines visual AI, orchestrated agents, and a learning engine into a single pipeline. Each layer solves a specific failure mode — and they compound.
Most tools convert your PDF to plain text before the AI ever touches it. Alembic skips that lossy step entirely. Your documents go directly to the AI as visual input — the same way you’d hand a page to a smart colleague and say “pull out the key terms.”
A single AI model running a single prompt is a demo. Production extraction requires coordination — one agent to classify, another to extract, another to validate, and an orchestrator to manage the whole pipeline. Alembic assigns the right model to each task automatically. Fast models handle simple lookups. Powerful models handle the hard stuff.
When you fix an extracted value, Alembic doesn’t just update the record. It creates a memory pattern — a persistent rule that ensures the same mistake never happens again. Patterns accumulate into a knowledge base specific to your documents, your terminology, your edge cases. The accuracy curve only goes up.
The point was never the extraction itself. It was always about what comes next — the approval, the payment, the flag, the decision. Alembic closes the loop between “data extracted” and “action taken.”
Upload a sample document and watch Alembic extract, validate, and structure your data in real time. No credit card. No sales call. No six-month implementation plan.
Start extracting — free