Extract Like a Pro — How DocLD Handles Your Messiest Documents

Invoices with line-item tables. Contracts with clauses and schedules. Multi-page forms where key fields hide in headers, footers, and sidebars. Most extraction tools treat every document the same: one pass, one prompt, hope for the best. At DocLD, we built something different. Intelligent extraction that adapts to document structure—so you get accurate, complete data from your most complex files without stitching together multiple tools or writing custom logic.

The Problem with One-Size-Fits-All Extraction

Everyday business documents are rarely a single block of text. An invoice has a vendor header, a line-item table, tax and totals, and sometimes terms at the bottom. A contract has parties and dates at the top, clauses in the middle, and signature blocks at the end. Forms spread fields across multiple pages with tables, checkboxes, and free text mixed together.

When extraction treats the whole file as one undifferentiated stream, things go wrong. Header fields get missed because the model is busy with body text. Line items get duplicated when the same table is interpreted multiple times, or fragmented when the model stops too early. Values from the wrong section end up in the wrong field—e.g. a clause number in the "amount" slot. The result is incomplete or noisy data that forces manual cleanup, breaks automations, or blocks compliance workflows.

DocLD's intelligent extraction is built for exactly this reality. Instead of one pass over the entire document, the system adapts to how your document is structured—so headers, tables, form blocks, and body text are each handled in a way that fits their role. You get one coherent, schema-aligned result: complete line items, correct totals, and every field in the right place.

From Chaos to Clean Data

Complex documents don't look like one big blob of text. They have titles, tables, key-value blocks, and paragraphs—each with different semantics. Generic extraction often misses fields, doubles line items, or mixes header data with body. DocLD's intelligent extraction is designed for exactly this: it understands document structure and extracts accordingly, so you get one coherent result instead of a patchwork.

No manual region tagging, no separate table parsers—just your document and your schema. The result is structured data you can trust for downstream workflows, reporting, and compliance.

Before and After: What Changes When You Turn It On

With standard extraction, complex documents often produce partial or messy output: missing line items, duplicate rows, or header data mixed into body fields. Intelligent extraction is built to reduce those failures by adapting to structure.

You use the same schema and the same API—only the extraction mode changes. No new integrations, no custom code. Turn it on when your documents have tables, multiple sections, or many fields; leave it off for short, simple docs where standard extraction already performs well.

What "Intelligent" Means in Practice

We don't treat every page the same. Our extraction recognizes that titles and headers carry different information than tables or form-like blocks or body text. That structural awareness means:

• Fewer missed fields — Important values in headers, footers, or sidebars are captured where single-pass extraction often drops them.
• Cleaner line items — Tables and list-like content are extracted as complete, deduplicated arrays instead of fragmented or repeated rows.
• One unified record — You get a single, merged result per document that respects your schema—no manual stitching.

Use cases range from invoices and receipts (vendor, totals, line items) to contracts (parties, dates, key clauses) to tax forms and applications (dozens of fields across many pages). If your docs have mixed layout, intelligent extraction is built to handle it.

What Makes a Document "Complex"?

Not every PDF needs intelligent extraction. Simple, single-page documents—a short receipt, a one-page form, a single-table spreadsheet—often work great with standard extraction. You'll benefit most from intelligent extraction when your documents have one or more of these traits:

When in doubt, try intelligent extraction on a few sample documents and compare results. Same schema, same workflow—you'll see quickly whether completeness and accuracy improve.

When to Use It

Simple, single-page documents (e.g. a short receipt or a one-page form) work great with standard extraction. When you see missing fields, duplicate or broken line items, or values from the wrong section, flip on intelligent extraction—same schema, better results.

Real-World Scenarios

Invoices and purchase orders. Vendors send PDFs with a header (vendor name, PO number, date), a large line-item table (description, quantity, price, extended amount), and a footer (subtotal, tax, total). Standard extraction often drops a few line items or pulls the wrong total. With intelligent extraction, you get a full line-item array, correct totals, and header fields in the right place—ready for ERP, approval workflows, or reconciliation.

Contracts and NDAs. Parties, effective date, and key terms often sit in headers or sidebars; clauses and schedules form the body; signature blocks sit at the end. Single-pass extraction can mix clause text into party fields or miss dates. Intelligent extraction keeps sections distinct so you get clean party names, dates, and clause-level data for comparison, redlining, or clause libraries.

Forms and applications. Tax forms, applications, and surveys mix tables, checkboxes, and free text across many pages. Fields repeat, sections repeat, and layout varies. Intelligent extraction is built to handle that variety—one schema, one run, one structured result you can feed into forms processing, underwriting, or compliance checks.

From Upload to Structured Data: The Big Picture

DocLD fits into your document workflow from upload through to downstream systems. Intelligent extraction is one step in that journey—the one that turns complex layouts into clean, schema-aligned data.

You upload documents via the dashboard, API, or CLI; they're parsed and chunked. When you run extraction with intelligent mode on, the result is a single JSON (or your schema shape) with field-level confidence and optional citations. From there you can push data into workflows, compare extractions, or send it to your own systems via API or webhooks.

Confidence and Traceability

Extraction isn't useful if you can't trust or audit the result. DocLD returns confidence scores per field and an overall confidence for the extraction, so you know which values to spot-check or send for review. When you need to show where a value came from—for compliance, dispute resolution, or debugging—you can enable citations: source text, page number, and optional bounding box so every field is traceable to a location in the document.

Intelligent extraction uses the same confidence and citation model as standard extraction. You get one merged result with per-field confidence and, when requested, evidence for each value. That makes it easier to build human-in-the-loop flows, pass audits, and improve schemas over time using ground truth and corrections.

How to Turn It On

In the dashboard

1. Go to Extract.
2. Choose your document and schema.
3. In Settings, enable "Agentic extraction (complex docs)".
4. Run extraction as usual.

Via API

Send config.settings.agenticExtractionMode: true with your extraction request (or set it on the schema so every run uses it). Full options are in the Extract API and extraction docs.

In batch and workflows

Use the same setting when you run batch extraction or when extraction is a step in a workflow. Enable it once on the schema and every document that uses that schema—single run, batch, or workflow—gets intelligent extraction automatically.

The Bottom Line

DocLD's intelligent extraction is built for the documents that break generic tools: tables, multi-page forms, and mixed layouts. One mode, one toggle—no custom pipelines or region tagging. Get cleaner, more complete structured data for invoices, contracts, and beyond. Try it on your hardest documents in the Extract page or via the API. For more on schemas, jobs, and corrections, see Structured Extraction in DocLD.

Frequently Asked Questions