Document Processing in Paper & Forest Products
Paper and forest products companies process enormous volumes of documents every day — timber harvest permits, log scale tickets, stumpage agreements, fiber supply contracts, chain-of-custody certificates, mill production reports, vendor invoices, and environmental compliance filings.
Slow or inaccurate document processing creates supply chain delays, compliance gaps, and errors in payment calculations that compound across procurement cycles.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
Why Agentic Document Extraction for
Paper & Forest Products
Traceable Fiber Supply Documentation
Every extraction is grounded to a precise location in the source document, giving procurement and compliance teams an auditable record of fiber origin claims across every supplier tier.
Accuracy Across High-Volume Mill Transactions
Mill operations process scale tickets, vendor invoices, and production reports in high volumes with dense numerical tables; accurate extraction eliminates payment discrepancies and data entry errors that erode margins.
Faster Regulatory and Certification Response
Harvest permit applications, environmental compliance filings, and chain-of-custody audit packages require organized, complete documentation; automating extraction compresses the time needed to assemble and submit these records.
Built for Complex Paper & Forest Products Documents
Intelligent document processing across timberlands management, sawmill and lumber operations, pulp and paper manufacturing, and wood fiber procurement is extremely difficult due to the sheer diversity of document types, the inconsistent layouts and the domain expertise required. Then add multiple languages, handwriting, photographs, scans and faxes to the complexity.
Accurate parsing of dense tables that span multiple pages and contain merged cells.
Single pipeline for image, slide, document, and spreadsheet file types with 1000+ pages.
Strong recognition of character-based languages, handwriting, checkboxes, stamps and signatures.
Schema-driven field extraction with visual grounding traceable to the original document.
Use cases
Fiber Procurement & Log Scaling
Extract volume, species, grade, and pricing data from log scale tickets, stumpage agreements, log purchase agreements, and fiber supply contracts to validate payment calculations and update inventory records.
Accelerate payment reconciliation and reduce disputes with suppliers
Eliminate manual data entry from scale tickets across high-volume procurement cycles
Maintain a verifiable, traceable record of every fiber transaction for audit readiness
Chain-of-Custody Compliance
Organize and verify fiber origin claims, supplier self-declarations, FSC and SFI chain-of-custody certificates, and third-party audit records required to demonstrate certified sourcing across the full supply chain.
Protect access to certified product markets by keeping compliance documentation current
Reduce the labor required to assemble audit packages for certification renewals
Surface documentation gaps before audits with automated completeness checks across supplier records
Harvest Permitting & Environmental Filing
Extract and organize key terms, conditions, and approval dates from timber harvest permits, forest practice applications, state agency correspondence, and environmental management plans to keep operations on schedule.
Shorten permit review cycles to reduce delays in harvest scheduling
Cut staff time spent manually tracking permit conditions and expiration dates
Reduce regulatory exposure by ensuring permit conditions are captured and flagged before operations begin
Trusted for Document-Heavy Paper & Forest Products Workflows
Agentic Document Extraction enables paper and forest products companies to automate document-intensive processes that traditionally require manual review.
Very often a loan officer who gets a borrower a solid preapproval fastest earns the deal and the real estate agent’s referrals. Reconstructing income is the hardest part, and Agentic Document Extraction is the cornerstone to getting it right and traceable. Accurate data upfront means we can get a cleaner loan file to the underwriter and get to CTC faster. Get it wrong and everything downstream gets affected.”
View case study →
Agentic Document Extraction has proven to be both accurate and easy to use. We are building on that foundation to deliver reliable, transparent, and scalable automation that our customers can validate and trust.”
View case study →
Trust is the product. Accuracy alone isn’t enough at enterprise scale—what matters is provenance, traceability, and control. LandingAI gives us confidence that every extracted value can be traced back to its source, audited, and defended. That’s what makes it deployable in regulated, real-world environments.”
View case study →Our Plan Review Agent has a lot of complicated components under the hood: traversing building code knowledge graphs, reasoning across disciplines and sheets, assessing issues informed by historical projects. None of it works if we can’t trust what came off the page. ADE gave us a reliable foundation, so our team could focus on incorporating our team’s expertise into our compliance reasoning system.”
View case study →
ADE has significantly outperformed other document extractors we’ve used. It has helped us build an Agentic RAG answer engine, based on unique healthcare institutional content, to offer instant, validated support to medical professionals at the point of care.”
View case study →
I appreciate its reliability and the fact that they're constantly innovating with new models, which helps us work smarter. The service is essential for handling heavy workloads in financial institutions as it provides the necessary infrastructure for high accuracy and fast throughput. I also find it adaptable to specific use cases because they're always working on new models.”
We use LandingAI's Agentic Document Extraction to build pipelines that turn unstructured text into structured data. First, the NER (Named Entity Recognition) detection has amazing accuracy. Second, the OCR capability is excellent — earlier I had to run a separate PDF extractor for text plus a separate LLM with OCR to summarize images, and now it's one step. Third, the image boundary detection is a standout.”
We ran a structured bake-off: the same five PDFs (ranging from a 12-page slide deck to a 400-page machinery manual) processed through other products and Landing AI's Agentic Document Extraction (ADE). We scored each tool on four criteria: Table fidelity, Figure extraction, Chunk typing, Scale. Landing AI ADE was the only tool that scored well on all four.”
Very often a loan officer who gets a borrower a solid preapproval fastest earns the deal and the real estate agent’s referrals. Reconstructing income is the hardest part, and Agentic Document Extraction is the cornerstone to getting it right and traceable. Accurate data upfront means we can get a cleaner loan file to the underwriter and get to CTC faster. Get it wrong and everything downstream gets affected.”
View case study →Agentic Document Extraction has proven to be both accurate and easy to use. We are building on that foundation to deliver reliable, transparent, and scalable automation that our customers can validate and trust.”
View case study →Trust is the product. Accuracy alone isn’t enough at enterprise scale—what matters is provenance, traceability, and control. LandingAI gives us confidence that every extracted value can be traced back to its source, audited, and defended. That’s what makes it deployable in regulated, real-world environments.”
View case study →Our Plan Review Agent has a lot of complicated components under the hood: traversing building code knowledge graphs, reasoning across disciplines and sheets, assessing issues informed by historical projects. None of it works if we can’t trust what came off the page. ADE gave us a reliable foundation, so our team could focus on incorporating our team’s expertise into our compliance reasoning system.”
View case study →ADE has significantly outperformed other document extractors we’ve used. It has helped us build an Agentic RAG answer engine, based on unique healthcare institutional content, to offer instant, validated support to medical professionals at the point of care.”
View case study →I appreciate its reliability and the fact that they're constantly innovating with new models, which helps us work smarter. The service is essential for handling heavy workloads in financial institutions as it provides the necessary infrastructure for high accuracy and fast throughput. I also find it adaptable to specific use cases because they're always working on new models.”
We use LandingAI's Agentic Document Extraction to build pipelines that turn unstructured text into structured data. First, the NER (Named Entity Recognition) detection has amazing accuracy. Second, the OCR capability is excellent — earlier I had to run a separate PDF extractor for text plus a separate LLM with OCR to summarize images, and now it's one step. Third, the image boundary detection is a standout.”
We ran a structured bake-off: the same five PDFs (ranging from a 12-page slide deck to a 400-page machinery manual) processed through other products and Landing AI's Agentic Document Extraction (ADE). We scored each tool on four criteria: Table fidelity, Figure extraction, Chunk typing, Scale. Landing AI ADE was the only tool that scored well on all four.”
