Problem to production.
SAPR builds production agentic AI systems. Every project below went from a real business problem to a deployed solution running on real data. These examples are document-heavy, but the same approach, ingest anything, reason, act, extends to live streams, sensors, and any source.
Academic Transcript Ingestion System
Goal
Clear the admissions and transfer-credit backlog created by manually processing thousands of transcripts in dozens of incompatible formats.
What the agents do
Read, normalize, and reconcile any transcript format, then post results straight into the SIS, cutting processing time dramatically.
Financial Document Data Extraction
Goal
Free the operations team from manually keying critical data out of PDFs, statements, and reports, slow, expensive, and error-prone at scale.
What the agents do
Extract, validate, and route structured data from financial documents at 98.5% accuracy, with no manual entry.
Job Skills Matching
Goal
Stop losing qualified candidates to keyword search and manual review that miss anyone who describes their skills differently.
What the agents do
Reason over skills semantically to surface qualified candidates by genuine relevance, beyond keyword overlap.
Insurance Claims Document Automation
Goal
Clear the intake bottleneck on 40,000+ claims a year arriving as document bundles, medical records, invoices, adjuster notes, all needing manual extraction.
What the agents do
Classify, extract, and route every claim document end to end at 96.2% accuracy, cutting cycle time from 8.7 to 2.4 days.
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