A document-AI pipeline that turned stacks of varied-format timecards into clean, validated payroll data — automatically.
A leader in entertainment payroll processed enormous volumes of timecards, often in inconsistent and handwritten formats, largely by hand — slow and error-prone.
Timecards vary wildly in format and quality. Extracting accurate data from them at scale meant combining reliable OCR with AI that could interpret messy, inconsistent documents and flag what it wasn't sure about.
We built an OCR layer to digitize timecards across formats, including difficult and handwritten ones.
AI extracts and structures the relevant fields, interpreting inconsistent layouts rather than relying on rigid templates.
Automated validation catches errors, and a human-in-the-loop step handles the exceptions the system flags as uncertain — accuracy without full manual review.
Entertainment-industry timecards come in a wide range of formats — different productions, different unions, different paper and digital templates — and the starting point was stacks of these in formats that varied enough that a single rigid template-matching approach wouldn't hold up. The document-AI extraction layer is built to handle this variation: it identifies the relevant fields (worker identity, hours, rates, job codes, date ranges) across different layouts rather than requiring each format to be pre-registered as a known template, using a combination of layout analysis and field-level extraction models.
Extracted data isn't automatically correct — a misread digit, a field extracted from the wrong location on an unusual layout, or a timecard that's internally inconsistent (hours that don't sum correctly, a date outside the expected pay period) all need to be caught before the data reaches payroll. The validation layer applies business rules specific to entertainment payroll (union rate rules, overtime calculations, job-code validity) and flags discrepancies for human review rather than passing everything through silently — the goal was to catch errors before they became payroll mistakes, not to eliminate human review entirely.
Once extracted and validated, data from wildly different source formats is normalised into a standard schema that the payroll system can consume directly. This is where the 'stacks of varied-format timecards become clean payroll data' transformation actually happens — the payroll system doesn't need to know or care what the original timecard looked like, because by the time data reaches it, every record has the same shape.
Timecard processing became faster and more accurate, with manual effort focused only on genuine exceptions — freeing the payroll team from rote data entry.
Tell us what you're building. We'll tell you the fastest honest path to shipping it.
Start a conversation →