End manual data entry for legacy inspection archives. The Vision-LLM engine ingests decades of unstructured PDF reports and automatically extracts CML wall-thickness readings, asset tags, and inspection dates — turning a multi-month onboarding into a multi-day exercise.
Digitalization leads, integrity engineers, and program managers responsible for onboarding brownfield facilities with 20–40+ years of historical inspection records stored as scanned PDFs, vendor reports, and turnaround binders. Built for organizations that cannot afford to leave decades of CML data trapped in static documents while their RBI program runs on a thin slice of recent measurements.
Drop entire turnaround binders or vendor report archives into the upload queue. The pipeline runs OCR across scanned pages, mixed-orientation tables, and handwritten annotations. Each document is processed in parallel with a per-document status feed.
The Vision-LLM identifies asset tags (e.g. V-101, E-204A, P-507B) across naming conventions and abbreviation styles. Each extracted tag is matched against the platform's asset registry — unmatched tags surface for engineer review rather than silent loss.
CML identifiers and wall-thickness measurements are extracted from inspection tables with date alignment. Multi-page reports are stitched into a single chronological reading set per asset. Engineers review a confidence-scored side-by-side view before any reading is committed.
Every extracted value carries a confidence score. Low-confidence extracts are flagged for engineer verification. Accepted readings flow into the immutable audit log with the source document referenced — full provenance from PDF page to risk calculation.
Bootstrap an RBI program with decades of historical condition data in days, not months. Facilities that would otherwise begin RBI calculations with only their most recent turnaround now have a full corrosion-rate history per CML. Inspection intervals derived from the resulting RBI scores are based on actual long-term trends, not on the thin sliver of post-digitalization data that legacy programs typically have to work with.
We will process a representative sample of your historical PDFs at no charge.