Home / Case Studies / AI Invoice Extraction
/ Case Study · Information Technology
AI Invoice Extraction, Wired into ERP. Manual Data Entry, Eliminated.
/ The engagement
Automated Invoice Data, End to End.
An automated invoice data extraction and management solution that removes manual entry, reads varied invoice formats with machine learning and OCR, and feeds clean data into automated business processes.
/ About the client
Manual invoice processing is tedious and prone to human error. Companies need solutions built for scale, cost reduction, accuracy, and compliance, and AI now makes that automation practical.
Brainvire built an automated invoice data extraction and management solution that removes the manual work, reads a wide range of invoice formats, and enables automated business processes on top of clean, structured data.
/ The approach
Design, architecture, and features, shipped as one program.
Enough to learn from
We addressed thin training data so the model could capture the complexity of real-world invoices rather than a narrow sample.
Reading poor scans
PaddleOCR lifts extraction from low-resolution images and documents, so quality no longer blocks the pipeline.
Structured and not
The model handles both structured and unstructured invoice formats, taming the variability that usually breaks extraction.
Kept within bounds
We tuned machine learning models to hold accuracy high and keep outcomes within acceptable bounds across every run.
/ What stood in the way
Four problems, solved without a maintenance window.
Select a challenge to see how it shaped the build.
Insufficient training data
Thin data hindered the model's ability to capture real-world complexity. We addressed the gap so the model learned from a fuller picture of invoices.
Poor document quality
Existing data was full of low-quality, low-resolution images and documents. We used PaddleOCR to raise extraction quality from those poor scans.
Variable, unstructured invoices
Structured and unstructured invoice formats varied widely and increased complexity. We built the model to standardize processing across formats.
Reduced accuracy
Reduced accuracy risked unreliable outcomes. We streamlined machine learning models to achieve high accuracy and keep results within acceptable bounds.
/ Product screens
The delivered experience.
The interface people actually use.
/01Extraction Accuracy
/02OCR Quality
/03Standardized Data
/04Streamlined Flow/ The results
What changed, in numbers.
/ Next step
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