Extracting data from any document
- ❌ The problem: Client sends PDF invoices, receipt photos, cash register slips, all mixed. Each document has a different layout. Traditional OCR fails constantly.
- ✅ The solution: AI that understands financial documents regardless of format - reads, interprets, and extracts tax IDs, amounts, dates, even from crooked photos or poorly formatted PDFs.
📊 Typical result: 90% of documents extracted automatically, including photographic receipts and non-standard invoices.
Classifying entries automatically
- ❌ The problem: "Payment to John Smith, 350€" - is it supplier, freelancer, or reimbursement? Requires knowledge of client context that traditional software lacks.
- ✅ The solution: AI that learns each client's context and classifies automatically: "John Smith = material supplier, account 62.1.1, based on history".
📊 Typical result: 85% of entries classified automatically based on context and history.
Reconciling when descriptions do not match
- ❌ The problem: Bank statement says "SEPA TRF COMPANY ABC", invoice says "ABC Ltd - Invoice 2024/156". Traditional software cannot match without exact reference.
- ✅ The solution: AI that does fuzzy matching: understands that similar amounts, approximate dates, and similar names indicate the same transaction.
📊 Typical result: 80% of reconciliations done automatically, even without exact references.
Detecting anomalies and errors
- ❌ The problem: Duplicate invoice, inconsistent amount, miscalculated VAT. With hundreds of documents per month, errors go unnoticed.
- ✅ The solution: AI that analyzes each document in client context and alerts: "This invoice has the same amount and date as another from 2 weeks ago - possible duplicate."
📊 Typical result: 95% of errors and anomalies detected before posting.
Answering client questions
- ❌ The problem: Client asks "how much did I spend on marketing this year compared to last year?" You have to open reports, filter, calculate.
- ✅ The solution: AI that answers natural language questions about client data, generating on-demand analyses.
📊 Typical result: Any analytical question answered in seconds, with data and context.
Interpreting client emails
- ❌ The problem: Client sends email: "here is that supplier invoice, and by the way check if everything is ok with the quarterly VAT". Multiple requests in one informal email.
- ✅ The solution: AI that interprets emails, identifies all requests (even implicit ones), and creates organized tasks automatically.
📊 Typical result: Zero client requests lost or forgotten in long emails.
Next Step
Accounting deals with information that comes in every imaginable format. AI can finally process the chaos. Choose one problem to start.
