Databricks said its new Precision Mode for the ai_extract API is now available, targeting hard document-extraction jobs such as 2,000-page files, invoices with thousands of line items and schemas with 300-plus nested fields.
94.7% accuracy on roughly 9,000 benchmark documents is the headline result, with Databricks saying the tool beat the strongest chunk-and-merge frontier-model baseline—GPT-5.6 Sol—by 7 percentage points.
The system combines custom-trained extraction models with an agentic harness that breaks large jobs into smaller parallel tasks, preserves intermediate results and reconciles them into one structured output.
Databricks argues that setup avoids common failure modes in general-purpose LLM workflows, including context-limit misses, chunk timeouts, truncated outputs and incomplete merges on long, reasoning-heavy documents.
Customers including Panasonic, EY-Parthenon and Intercontinental Exchange already use Databricks Document Intelligence at scale, as enterprises push to turn unstructured documents into data for production pipelines, agents and applications.
How does Databricks' Precision Mode balance the computational cost of its agentic harness against the promised accuracy for complex enterprise documents?
Will the parallel processing approach in Precision Mode sacrifice macro-level context while breaking massive documents into smaller extraction tasks?
Is this new agentic harness a permanent solution for long-document extraction or merely a temporary patch for current LLM context window limits?