Expenses,
sorted on arrival
Stop manually tagging transactions — your financial data should work the moment it lands
Pyvantir applies machine learning to incoming expense data and assigns categories with the same logic your accounting team uses — only faster and without the queue. Over 94% of transactions are correctly classified on the first pass, with low-confidence items flagged for a single-click review.
Where categorization breaks down
Most finance teams deal with the same friction: expense reports arrive with vague descriptions, multiple currencies, and inconsistent merchant names. A transaction labelled "SQ *COFFEE" could be a team lunch, a client meeting, or an office supply run depending on context. Without that context, every ambiguous entry costs someone 3–4 minutes of investigation.
Pyvantir's model reads merchant data, amount ranges, timing, and your existing category rules to make an informed call. It learns from your corrections over time, so the longer it runs the fewer edge cases end up in the review queue.
Full service details
Automated rules meet human-trained context
Works with your chart of accounts
Map categories to your existing GL codes. No need to rebuild your accounting structure.
Real-time processing
Transactions are categorized within seconds of import — no overnight batch jobs.
Confidence scoring on every line
Each categorization carries a score. Items below your threshold land in a dedicated review list.
Numbers from actual deployments
These figures come from client accounts active for at least 6 months, averaged across 3 different business sizes and industries.
From raw data to clean ledger
The categorization process has 4 distinct stages. Each one handles a specific part of the problem — data ingestion, contextual classification, confidence review, and sync to your accounting platform.
Connect your data source
Pull transactions from bank feeds, credit card exports, or accounting tools via API. Pyvantir accepts CSV, OFX, and direct integrations — setup takes under 30 minutes for most clients.
Model reads context, not just description
The classification engine weighs merchant name, MCC code, transaction amount, time of week, and your historical patterns. A S$14.80 charge from a cafe at 8am is very different from the same amount at 11pm.
Low-confidence items queue for review
Anything below your set confidence threshold goes into a focused review list — typically under 7% of transactions. One-click corrections update the model so the same pattern won't repeat.
Approved data syncs to your books
Categorized transactions push back to your accounting platform with the correct GL codes, reference numbers, and cost centre tags already applied. No copy-paste, no reformatting.
Context-aware classification in real time
Focused review list — only what needs a human eye
What using it actually feels like
"We were spending about 9 hours a week just matching expenses to the right cost centres. Three months in, that's down to under 40 minutes — and most of that is spot-checking."
Wren Balthazar manages finance operations for a mid-size logistics firm with 4 active entities across the region. Before switching to automated categorization, her team was running monthly reconciliation over 3 days. The first full month with Pyvantir closed in a single afternoon.
Wren Balthazar
Finance Operations Manager
Reconciliation that used to take days
Start a conversation
Got a specific categorization problem or want to see the model working on a sample of your own transactions? Send a note and we'll set up a short working session — no slide decks, just a live look at your data.
Based in Singapore, serving clients nationwide