School districts process enormous volumes of documents every day—student enrollment forms, Individualized Education Programs, immunization records, free and reduced-price meal applications, teacher credential files, 504 accommodation plans, federal grant documentation, and student cumulative records, the list goes on.
Documentation gaps across enrollment, special education, and program compliance create audit exposure that oversight agencies surface at every review.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
Every extracted student record field is grounded to a precise location in the source document, giving district administrators the audit trail that FERPA reviews, IDEA compliance monitoring, and federal program audits require.

Student records arrive from prior schools, healthcare providers, and state agencies in formats that vary widely across districts and document generations; agentic extraction handles all of them without institution-specific configuration.

Compressing document processing cycle times during enrollment, IEP evaluation, and federal program intake ensures students receive services without administrative delays that affect access to instruction, meals, and accommodations.

Intelligent document processing across student enrollment, special education administration, district HR and finance, and federal program compliance 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.
Extract evaluation findings, goal statements, accommodation requirements, and parental consent records from IEP documents, special education evaluation reports, 504 plans, and prior written notices to support timely IDEA compliance and caseload management.
Extract student identity, residency, health, and prior academic history from enrollment forms, proof of residency documents, immunization records, and student cumulative records to populate student information systems accurately at district entry.
Extract eligibility criteria, household income data, and student demographic information from free and reduced-price meal applications, McKinney-Vento intake forms, Title I parent notifications, and English Language Learner assessments to support federal program reporting and funding qualification.
Agentic Document Extraction enables K-12 school districts to automate document-intensive processes that traditionally require manual review.
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.”
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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 →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 →
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 →
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 →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 →