AI Document Intelligence (Legacy PDF Ingestion)
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.
Who It Is For
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.
Capabilities
Bulk PDF Upload & OCR
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.
Named Entity Recognition for Asset Tags
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.
Wall-Thickness Reading Extraction
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.
Confidence Scoring & Human-in-the-Loop
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.
Outcome
Bootstrap an RBI program with decades of historical condition data. 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.
Key Capabilities
- Vision-LLM pipeline for PDF, scanned image, and mixed-format ingestion
- Bulk upload with per-document status tracking
- Named Entity Recognition for asset tag and CML identifier extraction
- Wall-thickness reading extraction with date and unit normalization
- Multi-page report stitching into chronological reading sets
- Confidence-scored side-by-side review UI for engineer verification
- Provenance chain from source PDF page to committed reading
- Audit-log integration with source-document hash referenced
Strategic Benefits
- RBI calculations based on full historical trend, not a recent slice
- Eliminate the engineer-hours otherwise spent re-keying PDF data
- Reduce data-entry errors with confidence-scored extraction
- Defensible provenance — every reading traces back to its source page
Pilot Document Intelligence on Your Archive
We will process a representative sample of your historical PDFs at no charge.