LandingAI’s Agentic Document Extraction Gen2 introduces a new pricing model and two parsing engines that address common pain points for teams processing large volumes of PDFs, scans, and forms. The first problem is unpredictable cost. Older solutions charged a flat fee per page, making dense documents expensive and sparse ones cheap. Gen2 splits the bill into a per‑page component plus a per‑character output charge, letting users see exactly what drives the cost and choose a cheaper model (Verity) for simple text or a richer model (Pro) for complex layouts, handwriting, or math. Teams can also switch to the Standard tier for asynchronous jobs, cutting the price in half.
The second problem is brittle output. Earlier versions returned a flat list of chunks that lost hierarchical context, making it hard to locate a specific value or to rebuild the original layout. Gen2 returns a tree structure with pages, blocks, and atomic grounding that points every extracted word or line back to its exact location on the source page. This enables precise PII redaction, reliable document diffing, and reviewer interfaces that highlight the exact source rather than an approximate region.
The third problem is deployment flexibility. Many regulated industries cannot send data to public clouds. Gen2 can run in the LandingAI playground for free testing, in US or EU cloud, inside a VPC on AWS, Azure or Google Cloud, within Snowflake, or fully on premises, including air‑gapped environments. The Ground API consumes no credits, allowing validation without extra cost.
By picking the right model, tier, and deployment option, teams can reduce document processing spend by 25‑80 %, gain traceable results, and keep data where compliance demands it.
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