Your DOCX is already structured data.
Why are you destroying it?

Every tool that converts Office documents to PDF before parsing throws away the very structure you need.

The Round-Trip Problem

A DOCX file is a ZIP archive of XML. Track changes, comments, merged cells, headers, footers, speaker notes — they are all right there in the markup, addressable and typed. Converting to PDF flattens this into a bitmap of positioned glyphs. Then an ML model has to guess the structure back from pixel positions.

PDF-first parsers
DOCX (structured XML)
↓
Convert to PDF — structure destroyed
↓
ML model guesses layout from pixels
↓
Approximate output (63% accuracy)
AILANG Parse
DOCX (structured XML)
↓
Read XML directly — structure preserved
↓
Exact output (93.9% composite)

Two lossy steps versus zero. A categorically different approach.

Feature-by-Feature Comparison

What each tool preserves when parsing a DOCX, PPTX, or XLSX file. Data from OfficeDocBench (March 2026).

Feature AILANG Parse Unstructured Docling MarkItDown
Parsing method Direct XML PDF conversion + ML PDF conversion + ML mammoth / pandas wrappers
Track changes ✓ Full (author, date, type) ✕ None ✕ None ✕ None
Merged cells ✓ Structural (colspan) ✕ Flattened ✕ Flattened ✕ Flattened
Comments ✓ Author-attributed ✕ Dropped ✕ Dropped ✕ Dropped
Headers / footers ✓ Per-section ✕ Lost ✕ Lost ~ Basic
Text boxes ✓ Preserved ✕ Dropped ✕ Dropped ✕ Dropped
Speaker notes (PPTX) ✓ Preserved ✕ Dropped ✕ Dropped ✕ Dropped
Runtime deps ✓ None Python + heavy libs Python + PyTorch Python + mammoth
Browser / WASM ✓ Yes ✕ No ✕ No ✕ No
Speed (DOCX) <11 ms ~2 s ~3 s ~500 ms
OfficeDocBench (composite) 93.9% 62.1% 63.3% 67.9%
Coverage-adjusted 93.9% 38.7% 38.5% 51.2%

Every row with a ✕ represents data that existed in the source file and was thrown away during conversion. For legal, compliance, and audit workflows, this is not acceptable.

Using Alongside Other Tools

PDFs need ML — there is no XML to read. But Office documents are not PDFs, and routing them through a PDF pipeline destroys information for no reason.

The practical architecture: use AILANG Parse for Office formats where it reads the XML directly, and your preferred tool for PDFs where ML is genuinely needed.

Before: everything through one ML pipeline

# Everything through a PDF-first pipeline
from unstructured.partition.auto import partition

# Converts DOCX to PDF internally,
# then runs ML on the PDF.
# Track changes, comments, merged cells: gone.
elements = partition(filename="report.docx")

After: right tool for each format

from ailang_parse import AilangParse
from unstructured.partition.pdf import partition_pdf

if filename.endswith(('.docx', '.pptx', '.xlsx')):
    # Direct XML parse, no per-page billing, full structure
    blocks = AilangParse().parse(filename)
else:
    # ML pipeline where it's actually needed
    elements = partition_pdf(filename)

Keep your PDF pipeline exactly as it is. Add AILANG Parse for the formats where deterministic parsing produces categorically better results.

AILANG Parse also handles PDFs — by delegating to a pluggable AI model (Gemini, Claude, or local Ollama). If you want a single tool for everything, it works. The split approach is for teams that already have a PDF pipeline they are happy with.

See for Yourself

Upload a DOCX with track changes, a PPTX with speaker notes, or an XLSX with merged cells. Compare the output with whatever you are currently using.

Try in Browser API & SDKs

Frequently Asked Questions

What exactly is lost when converting DOCX to PDF before parsing?

Converting DOCX to PDF destroys five categories of data: (1) track changes and revision history, (2) merge attributes on table cells, (3) comments and annotations with author metadata, (4) semantic heading levels (PDF has no heading concept), and (5) document metadata like custom properties. AILANG Parse preserves all five by reading Office XML directly.

Why do wrapper-based parsers have quality problems with DOCX parsing?

Most wrapper-based parsers use libraries designed for display rendering, not structural extraction. They miss track changes, flatten merged cells, and drop comments. On OfficeDocBench, the next-best alternative scores 71.1% composite (68.0% coverage-adjusted) versus AILANG Parse's 93.9% with 100% format coverage.

Is PDF parsing always worse than direct DOCX parsing?

When the original source is a DOCX, direct XML parsing is strictly better — more accurate, no per-page billing, and preserves metadata that PDF rendering destroys. But if your source is a born-digital PDF or a scanned document, you need an ML-based PDF parser. AILANG Parse handles both: deterministic XML extraction for Office formats and pluggable AI backends (Gemini, Claude, Ollama) for PDFs.