The problem. Back-office staff manually re-key data from incoming forms, contracts, submittals, and inspection reports before it can be used downstream — in a project tracker, a billing system, or a compliance log. The documents arrive in inconsistent formats: scanned PDFs, photographed forms, spreadsheets with slightly different layouts each time. Manual entry is slow, and it is where transcription errors quietly enter the system.
The solution. An AI pipeline is built to extract the specific fields a given document type needs — line items from a contract, measurements from an inspection report, dates and signatures from a form — and structure them into the format the downstream system expects. The pipeline validates what it extracts against expected ranges and formats, and instead of guessing on anything ambiguous, it flags the exception for a human to review. The goal is not to remove the human from the loop; it is to remove the repetitive, error-prone parts and leave the judgment calls to a person.
The outcome. Staff review flagged exceptions instead of manually keying every document from scratch. Turnaround from document received to data usable downstream shortens, and the exceptions that do reach a human are the ones that actually need judgment, not routine transcription.
AI vendor InteliScience published a case study describing a private construction firm whose project documentation — gathering records, compliance details, forms, and supporting documents — took roughly a week per project by hand. Per InteliScience's published case study, an OCR-plus-validation pipeline cut that down to about an hour, while keeping a human in the loop for anything the system flagged as incomplete or inconsistent. Note this is the vendor's own account of an anonymized client, not an independently audited result — cited here as an example of the category, not a claim about a specific verified outcome.
