B2B Document Extraction: Rule-Based Systems vs. LLMs – A Real-World Comparison

A new practical comparison between rule-based PDF extraction using pytesseract and an LLM-based approach with Ollama and LLaMA 3 has been published, based on a realistic B2B order scenario. The analysis reveals critical trade-offs in accuracy, cost, and implementation complexity.

The study, conducted by a data scientist specializing in document processing, tested both methods on identical invoices and purchase orders from a mid-size manufacturer. Rule-based extraction correctly parsed 89% of fields, while the LLM approach achieved 94% accuracy but required significantly more computational resources.

“Rule-based systems are reliable for structured documents but fail when format varies, such as with different suppliers,” the researcher told TechAI News. “LLMs offer flexibility and can handle diverse templates, but they require careful prompt engineering and are slower for high-volume processing.”

For background on why this comparison matters, read on. Then see what this means for businesses.

Background

B2B document extraction is a critical task for automating order processing, invoice matching, and supply chain management. Traditional rule-based systems rely on predefined patterns such as regex, keyword search, and coordinate-based extraction from PDFs. These systems are fast and deterministic but brittle when confronted with layout changes.

LLM-based extraction, by contrast, uses large language models to understand context and extract relevant information without explicit rules. However, it introduces latency, cost per query, and potential hallucination issues. The study used a dataset of 500 B2B documents, each with 15–25 fields.

What This Means

The results indicate that no single approach is universally superior. For companies with highly standardized document templates, rule-based extraction remains cost-effective and fast. For organizations dealing with heterogeneous suppliers and frequent format changes, LLMs provide a more adaptable solution.

“The choice depends on the specific use case,” the researcher added. “Hybrid pipelines that combine rule-based pre-filtering with LLM-based fallback could offer the best of both worlds.” Analysts predict that as LLMs become cheaper and more reliable, hybrid models will dominate the document extraction market by 2026.

Key takeaways from the comparison:

• Accuracy: LLM (94%) vs. Rules (89%)
• Speed: Rules (0.12s/doc) vs. LLM (2.3s/doc)
• Cost: Rules (free, open-source) vs. LLM (variable compute + inference)
• Flexibility: LLM excels at varied layouts; rules require manual updates

Full details of the methodology and code are available in the original study. Businesses evaluating document automation solutions should consider these trade-offs carefully.