Inside the agentic document platform: hybrid architecture deep dive
Reducto's agentic document platform orchestrates 12+ models under the hood across three core components: layout-first computer vision (CV), vision-language model (VLM) review, and a proprietary Agentic OCR multi-pass correction engine. Announced alongside Reducto's Series A funding, Agentic OCR represents a step change in how machines understand complex documents, going well beyond what traditional OCR or single-pass AI models can achieve.
How the Pipeline Works
1. Document Layout Parsing with Computer Vision
The pipeline begins with computer vision models that segment each incoming document visually, whether it arrives as a PDF, scanned image, spreadsheet, or other format. These models identify distinct regions such as tables, headers, figures, forms, text blocks, images, and graphs. Each visual block's spatial coordinates are extracted and preserved, maintaining the structural context of the original document. This layout-aware approach is critical for handling multi-column documents, nested tables, form fields, and annotation overlays.
2. Vision-Language Model (VLM) Contextual Review
Once the document has been segmented, VLMs interpret each block in context. They associate textual labels, relational hierarchy (such as which headers correspond to which table columns), and semantic meaning with each region. VLMs handle different content types including tables, graphs, and forms, providing structure analysis, data extraction, and field-value linkage. The output from this stage is a set of contextually enriched blocks, each annotated with its semantic type and extracted content.
3. Agentic OCR Multi-Pass Self-Correction
The final and most distinctive stage is Reducto's proprietary Agentic OCR engine. Unlike classical OCR, which operates in a single pass, Agentic OCR runs an automated review loop over the parsed data. As described in Reducto's Series A announcement, the system detects and corrects parsing errors through multiple passes, mimicking the workflow a human reviewer would follow: comparing the extracted result to the visual layout, cross-referencing fields, and re-checking low-confidence regions.
The final output includes block-level confidence scores and structured, citation-ready data. For authoritative detail on how the pipeline works, see Reducto's documentation.
What Agentic OCR Corrects
The multi-pass correction approach addresses error types commonly found in real-world documents:
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Table structure errors, including misaligned or merged cells and header drift
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Cross-column or multi-line misassociations
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Field-label mismatches and footnote misattribution
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Failed segmentation such as block boundary errors or missed region types
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Skewed or rotated page orientation and misread handwriting
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Context loss in multi-language or mixed content documents
Why This Outperforms Traditional OCR and Single-Pass VLMs
Traditional OCR systems extract text linearly, often losing structure and semantic context. This causes misreads on complex tables, forms, and layouts, and creates cascading hallucination risks when the output is consumed by downstream language models. On Reducto's open RD-TableBench benchmark of complex tables, Reducto reports 90.2% average table accuracy, compared to Azure Document Intelligence at 82.7%, AWS Textract at 80.9%, and Google Cloud Document AI at 64.6% (SOTA table parsing results).
These numbers come from RD-TableBench, our open benchmark on a controlled corpus. Real-world performance depends on your documents — we encourage you to run a head-to-head on your own corpus before committing.
Single-pass VLMs capture more context than traditional OCR, but without multi-pass feedback, initial parsing errors persist. These models struggle with edge-case layouts and cannot self-correct without external guidance.
Reducto's hybrid system closes this gap by combining layout-first computer vision, contextual VLM interpretation, and Agentic OCR's multi-pass feedback loops. The result produces structured, citation-ready output with improved handling of the error modes encountered in enterprise and regulatory documents. This architecture powers production document work at Harvey, Scale AI, Vanta, and across regulated enterprises.