Document AI & expense workflows
From receipt photo to recorded expense.
Connected receipt OCR, RUC validation and AI-assisted categorization with expense registration.
Independent product designed and developed by Mathyu Cardozo.
Project
Monyx
Industry
Fintech / personal finance
Development
1 month
Stack
Next.js, TypeScript, Firebase, Receipt OCR, AI-assisted categorization
Evidence
Receipt to recorded expense
Mathyu Cardozo · AI Engineer Lead
The challenge
Receipt information needed extraction, issuer checks and categorization before registration.
What we built
OCR, RUC validation and AI-assisted categorization connected to expense registration.
What changed
Approximately 95% scanning accuracy, developer-reported; not accuracy of complete expense records.
Context and problem
Receipt information needed to move through reading, issuer validation and categorization into an expense record.
What we built
Implementation at a glance
Read the receipt information through OCR.
Validate the issuer RUC and support categorization with AI.
Register the expense after the defined checks.
Implementation details
An expense history depended on repeated entry of facts already printed on a receipt. Reading the document was only part of the task: issuer details needed a check, the purchase needed a useful category and the application still had to create an expense record. Monyx connected those responsibilities around Peruvian receipts.
Capture the document information
A receipt photograph entered the scanning workflow and OCR made the recognized purchase information available to the application.
Check and classify the result
The product validated the issuer RUC and used AI-assisted categorization. These operations answered different questions: issuer identification and expense classification.
Complete the registration
The application connected the processed information to automatic expense registration. Next.js, TypeScript and Firebase formed the reported product stack; the OCR provider and AI model are not specified.
Results and evidence
The developer reports approximately 95% receipt-scanning accuracy. Its definition, sample and evaluation conditions were not supplied. That figure applies to scanning as reported, rather than fully correct expense records. The delivered workflow joins extraction, issuer validation, categorization and registration, with the saved expense as the functional output.
Relevance and PDF contents
Where this pattern applies
This pattern applies to document-based operations where reading is followed by checking and manual entry. Start with one document type, the facts that must be checked and the record that the next task requires. The full PDF separates those completion boundaries so the workflow can be evaluated at each handoff.
Inside the full PDF
Explore the extraction, validation, categorization and registration workflow behind Monyx.
Receipt-to-expense functional workflow
Extraction, issuer validation and categorization boundaries
Destination record and reported accuracy limits
Document-workflow readiness worksheet
Free PDF · 7 pages · Includes a readiness worksheet.
On this page
Explore extraction, issuer validation, categorization and registration. Use the worksheet to map the checks in your own document workflow.
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