A data platform company that had relied for 15 years on ~70 human operators to manually read and extract data from complex government documents faced a chronic backlog.
Each document took six hours of human labor, delaying analysts and underutilizing college-educated staff doing rote data entry. The company asked for a 25% speed improvement, the real cost was in operational inefficiency and untapped data value locked inside unstructured documents.
Machine & Partners built a custom AI parsing pipeline for a government data platform company that had used 70 human operators for document extraction over 15 years. Using a mix of OpenAI and Anthropic models selected by task complexity, the pipeline eliminates manual data entry and stores extracted data in vector databases, enabling analysts to auto-generate reports in seconds.
Processing time per document dropped from 6 hours of manual labor to under 15 minutes of automated AI processing. Human involvement shifted from extraction to validation only.
Even the remaining human validation layer was cut by 75%, freeing college-educated operators to shift into analyst roles doing higher-value interpretation work.
Analysts who previously spent hours building reports manually can now auto-generate first-draft reports in seconds. Individual tasks went from 3 hours to 3 minutes; overall gains range from 3x to 20x.
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