Smart Content Ingestion for Generative AI Workloads
Quick Answer
This paper introduces a production-ready content-extraction system for generative AI, achieving a character error rate of 0.13% and table similarity of 0.995 on a 180-document corpus.
Quick Take
It emphasizes the importance of explicit and measurable content extraction, which is crucial for reliable AI reasoning across heterogeneous data formats.
Key Points
- The system features selective OCR routing and a curation engine for accurate extraction.
- Achieved 97.4/100 accuracy in character extraction and 68.6% hit@1 in chunking.
- Introduces three design principles: structure before semantics, never mutate measurements, and budget labels.
- Content extraction is positioned as the perception layer in enterprise AI systems.
- Performance metrics include mean reciprocal rank of 0.77 over 25,050 generated questions.
DeepSignal Analysis
What happened
A new content-extraction system for generative AI has been developed, achieving a character error rate of 0.13% and a table similarity score of 0.995 on a test set of 180 documents. This system emphasizes the need for precise content extraction to enhance AI reasoning across various data formats.
Key evidence
- The content-extraction system achieved a character error rate of 0.13% and a table similarity score of 0.995 on a corpus of 180 documents.
- The system includes features like selective OCR routing and a curation engine that measures character, word, and table-structure accuracy.
- The best extractor scored 97.4 out of 100, while the chunker achieved a hit@1 score of 68.6% and a mean reciprocal rank of 0.77 over 25,050 generated questions.
Why it matters
This development is significant as it addresses the challenges of content extraction in generative AI, where data is often heterogeneous and complex. By making content extraction explicit and measurable, it aims to improve the reliability of AI systems in reasoning over diverse data formats, which is crucial for enterprise applications.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:The evolution of machine learning has progressively changed where intelligence resides in an AI system. In conventional machine learning the task, data representation, labels and model architecture were tightly coupled, so data preparation was narrow, schema-bound and visible. Generative AI decouples the model from any single task: one foundation model serves open-ended downstream tasks, and the generality gained on the model side is matched by heterogeneity on the data side, because enterprise knowledge is authored in the formats people use (PDF, presentations, spreadsheets, scanned documents, forms, tables, diagrams and mixed-layout files) that carry textual, visual, geometric and structural information at once. A language model or retriever cannot reason reliably over information misrepresented at this interface, so content extraction becomes a lifecycle stage in its own right whose errors no downstream retriever or re-ranker can repair. This paper presents a production-ready content-extraction system that makes this stage explicit, configurable, and measurable. The system incorporates selective OCR routing, a scarcity-first curation engine with a reference-based extraction scorer that measures character, word, and table-structure accuracy, a deterministic structure-aware parent-child chunker, and a read-only retrieval evaluator that generates grounded questions from every page and reports Hit@k, mean reciprocal rank, and latency. On a 180-document corpus the best extractor scores 97.4 of 100 (character error rate 0.13%, table similarity 0.995) and the chunker reaches hit@1 of 68.6%, hit@10 of 92.8% and MRR 0.77 over 25,050 generated questions. We distil three design principles (structure before semantics, never mutate what you measure, budget your labels) and position measured content extraction as the perception layer of enterprise agentic systems.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.07091 [cs.AI] |
| (or arXiv:2610.07091v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07091 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Abbas Raza Ali [view email]
[v1]
Mon, 5 Oct 2026 13:22:01 UTC (161 KB)
— Originally published at arxiv.org
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