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Hire an LLM Engineer, Chicago
Chicago's manufacturing and insurance industries generate millions of pages of structured-but-unstructured documents every year: quality control inspection reports at Caterpillar and Boeing Chicago facilities, medical records and claims documents at CNA, Zurich North America, and Allstate. Manual data entry teams extract specific fields from these documents into downstream systems. An LLM pipeline does it at 10x the speed with 95%+ field-level accuracy.
The critical requirement for both industries: when the model is uncertain, it flags the record for human review rather than writing a wrong value. We build the confidence scoring and review queue into every pipeline.
Pricing depends on document types and integration complexity, and we scope it after seeing a sample document.
Tell us which documents your team extracts data from manually.
Quality control reports at a Chicago-area Caterpillar or ABB facility contain specific required fields: defect type, defect location on the part, severity rating, inspector ID, and disposition. These fields are filled in by inspectors as free-text narrative, not structured form fields. A data entry clerk reads each report and keys the values into the quality management system. At $25/hour, processing 500 reports per day costs $1,000/day for that single task.
An LLM extraction pipeline with schema validation handles the same 500 reports in under two hours, with confidence scores on each field. Ambiguous cases go to a human queue. Typical accuracy on well-formatted QC reports: 95 to 97% at the field level.
For Chicago insurance companies, the extraction problem is medical records and claims documents. Straight-through processing for auto and property claims requires extracting: claimant information, date of loss, damage descriptions, medical diagnosis codes, treatment dates, and bill amounts from a mix of PDFs, faxed documents, and scanned forms.
An LLM pipeline with document classification and per-type extraction schemas handles the variability in claims document formats. The output is a structured intake record ready for adjudication, with flagged records for the cases where extraction confidence falls below threshold.
Current state
With LLM pipeline
Routes each incoming document to the correct extraction template. Handles format variation across facilities and document vintages.
JSON Schema for every document type your team processes. Required fields, value types, and acceptable ranges defined before any model call.
Per-field confidence scores. Extractions below threshold go to human review queue rather than writing uncertain values.
A lightweight queue UI where reviewers see the original document alongside the model's extraction and the confidence score for each field.
Output writes to your existing QMS, claims system, or database via API. We map extracted fields to your schema.
Weekly sample-based accuracy report comparing extracted values against manual spot-checks. Tracks accuracy per document type over time.
Send us a sample document, describe the fields you extract, and tell us which system the data goes into. We will reply with a scope and rough cost within one business day.