Case Study | Stealth Company

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At a glance

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When managing large volumes of vendor invoices, companies often struggle to extract meaningful service-level data. Traditional tools fall short, capturing totals and line items but missing the context, pricing types, and critical services hidden within vendor-specific language. Our solution bridges that gap, using OCR and advanced language models.

The Challenge

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The project presented further challenges:

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Companies were overwhelmed with hundreds of scanned vendor invoices, making manual comparison slow and error-prone.
Extracting detailed service-level data — not just totals — was critical.
Each vendor used its own terminology, and invoices often included unrelated fees or noise data.

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The Solution

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To address these challenges, our team adopted a strategic and methodical approach:

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We built a smart extraction tool using Tesseract OCR and Meta’s Llama language model.
The tool:
- Automatically identified vendor metadata such as name and address from scans
- Detected relevant service names and pricing, even across inconsistent formats
- Filtered out irrelevant data, such as fees or non-comparable add-ons
- Stored clean data in a structured database for reporting and vendor benchmarking

The Results

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We achieved several notable outcomes:

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1,000+ invoices processed with 99,7% accuracy on extracting core service lines.
70% reduction in manual review time.
Full cross-vendor price comparison operational within 2 weeks of launch.

By combining powerful OCR with an advanced LLM, we enabled precise, scalable invoice intelligence, while helping businesses take full control of their vendor costs and unlock real value from their data.