Document Processing in Broadline Retail
Broadline retailers process millions of documents every day — purchase orders, vendor invoices, advance ship notices, trade promotion agreements, compliance certifications, import customs filings, chargeback deduction notices, the list goes on.
Slow, error-prone document processing delays payments, triggers chargeback disputes, and exposes retailers to compliance penalties that erode margins.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
Why Agentic Document Extraction for
Broadline Retail
Faster Vendor Payment Cycles
Accurate, automated extraction of invoice and purchase order data compresses three-way match cycle times, so accounts payable teams close periods faster and capture early-payment discounts consistently.
Defensible Compliance Documentation
Every extracted field is grounded to its precise location in the source document, giving compliance and audit teams a verifiable record across vendor contracts, product certifications, and import filings without manual spot-checking.
Scalable Supplier Onboarding
Processing vendor onboarding packets, routing guides, and product specification documents without manual data entry allows merchant and procurement teams to activate new suppliers faster and at higher volumes than headcount allows.
Built for Complex Broadline Retail Documents
Intelligent document processing across mass merchandise, warehouse club, department store, and specialty general merchandise 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
Vendor Invoice Reconciliation
Extract and match line-item data across purchase orders, vendor invoices, packing lists, and goods receipt records to automate three-way match and identify discrepancies before payment.
Accelerate payment cycles to capture early-payment discounts and improve vendor relationships
Reduce AP processing cost per invoice by eliminating manual data entry and exception handling
Build a complete, auditable transaction trail that supports dispute resolution and financial controls
Vendor Compliance Management
Extract and validate compliance data from vendor routing guides, product certifications, children's product certificates, and labeling attestations to confirm every supplier meets retailer and regulatory requirements before goods are received.
Accelerate new supplier activation by reducing manual review of onboarding documentation
Reduce chargeback losses by catching labeling, packaging, and documentation gaps before shipment
Maintain structured, traceable compliance records that support audit and regulatory inquiries
Trade Promotion Deduction Management
Extract deal terms, promotional allowances, and shipment data from trade promotion contracts, retailer deduction notices, and proof-of-performance documents to validate and dispute short payments.
Recover a greater share of disputed deductions by accelerating evidence assembly and submission
Reduce manual reconciliation effort across finance and trade marketing teams
Create an audit-ready record of every promotion agreement, deduction, and resolution outcome
Trusted for Document-Heavy Broadline Retail Workflows
Agentic Document Extraction enables broadline retailers 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.”
