Custom Document Intelligence: 90% Cost Savings at Massive Scale

Context

A leading publisher partnered with us to overcome a major challenge: how to digitize and intelligently process hundred's' of millions of historical document pages with exceptional accuracy and dramatically reduced costs compared to existing AI solutions.

The Challenge

The publisher had amassed over hundred's' of millions of pages of historical documents. Off-the-shelf solutions like Azure and Google Document AI presented significant hurdles:

1. High Cost: Cloud-based solutions typically charge around $10 per 1,000 pages per specific extraction task, and costs for complex, custom extractions can escalate up to $30 per 1,000 pages, making large-scale processing financially unsustainable.
2. Limited Accuracy: Existing AI struggled with specific document challenges such as degraded scan quality, complex layouts (multi-column text, margin annotations, footnotes), and custom extraction requirements.
3. Missing Business-Specific Fields: Many essential fields tied to business needs—such as internal classification markers, document-type-specific metadata, or customer-defined content zones—were not supported by any existing provider.

Moreover, critical document elements—such as margin annotations, content indexes, and low-quality scan detection—were not supported by standard solutions.

Our Custom Solution

We created a sophisticated suite of custom AI models, each uniquely designed for distinct document-processing tasks. Our tailored approach included:

• Training Data: Thousands of documents were manually annotated to generate robust training datasets, supplemented by synthetic data to enhance model comprehensiveness.
• Custom AI Models: We built specialized models to accurately extract and classify elements like margin notes, main text, page numbers, author names, document titles, footnotes, multi-column text structures, images, tables, content indexes, and chapter titles.
• Continuous Measurement: Rigorous benchmarking was conducted using human-labeled documents to ensure accuracy and consistent performance improvements.
• Strategic Optimization: Models were iteratively refined based on comprehensive performance insights, ensuring alignment with evolving business goals.

The Impact

• Cost Efficiency: Achieved a 90–99% reduction in processing costs compared to cloud-based providers.
• Superior Accuracy: Delivered accuracy improvements of 2–5% across all document-processing tasks.
• Revenue Growth: Enabled the publisher to unlock new revenue opportunities through advanced document digitization and processing capabilities.
• Internal AI Adoption: Successfully fostered widespread internal adoption of custom AI solutions.
• Strategic Asset: Processed data became a valuable resource, licensed by a prominent AI company for training large language models (LLMs).

Ready to Transform Your Document Intelligence?

Looking to reduce costs and improve accuracy in your large-scale document workflows? Our team can help you build custom AI solutions tailored to your business needs.

Schedule a Discovery Call to explore how we can support your transformation.