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.
Machine & Partners began the engagement with a rapid experimentation phase — spending the first week running targeted tests on sample government documents to validate AI extraction feasibility before committing to a full build. This de-risking step confirmed that the extraction problem was solvable and shaped the model selection strategy.
Over four months of iterative four-week development blocks, they built a custom AI parsing pipeline that reads unstructured government documents and extracts structured data points automatically. Rather than selecting a single AI model, Machine & Partners routed extraction tasks to a mix of OpenAI and Anthropic Claude models based on the specific complexity of each document type — balancing cost, accuracy, and reliability.
Human operators were redeployed from extraction to validation, and even the validation layer saw a 75% time reduction as AI output quality improved with iteration. A byproduct of the vector storage layer was particularly valuable: semantic meaning and relational data were captured alongside structured fields, enabling analysts to auto-generate first-draft reports in seconds — a capability that had not existed in the prior manual workflow.
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