Ertas for PDF Parsing and Transformation

    Parse scanned, native, and mixed-layout PDFs into structured AI-ready output with layout awareness, quality scoring, and multi-format export. Handle 700GB+ document archives with a visual pipeline — no custom scripts required.

    The Challenge

    Enterprise document archives contain diverse PDF types — scanned, native, mixed layouts, multi-column, tables, technical drawings. Basic text extraction misses structure. Parsing at 700GB+ scale requires automation with quality checks. Service providers handling client documents need a reusable parsing pipeline.

    The Solution

    Ertas Data Suite's PDF Parser (powered by Docling) handles diverse PDF types with layout awareness. Combined with Deduplicator, Format Normalizer, Quality Scorer, and multi-format export, it creates a complete document-to-AI pipeline.

    Key Features

    Data Suite

    Layout-Aware PDF Parsing

    Handles scanned, native, mixed, multi-column, and table-heavy PDFs via Docling integration. Preserves document structure — headings, tables, lists — not just raw text.

    Data Suite

    Quality Scoring Post-Parse

    Quality Scorer flags low-confidence extractions for review before downstream consumption. Catch parsing issues at the source rather than debugging model performance later.

    Data Suite

    Flexible Transform

    RAG Chunker for retrieval use cases, Train/Val/Test Splitter for model training. One pipeline, multiple downstream preparation paths.

    Data Suite

    Multi-Format Output

    JSONL, RAG chunks (markdown + YAML/JSON), CSV from a single pipeline. Feed downstream systems the format they expect without rebuilding.

    Example Workflow

    An AI consultancy receives 700GB of construction PDFs from a client who needs both a RAG-powered document search and a fine-tuned estimating model. They build a pipeline: File Import → PDF Parser → Deduplicator (fuzzy matching for near-duplicate documents) → Format Normalizer → Quality Scorer → branched output: RAG Chunker → RAG Exporter + JSONL Exporter. Two outputs from one pipeline: chunked knowledge base for RAG search and structured JSONL for fine-tuning. The same pipeline template is reused for the next construction client with minor configuration adjustments.

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