Expense management is one of those problems that looks simple until you're staring at 400 transactions from six different payment methods and three currencies, all needing to be sorted before month-end close.
At zuqytyi, we built a service around the specific difficulty of getting expense data right - not just sorted, but correctly labelled, consistently applied, and ready for the workflows your finance team already uses. The system learns from your chart of accounts and gets sharper over time.
Talk to our team About zuqytyi
Each service below addresses a different point of failure in the expense categorisation process - from raw transaction ingestion to audit-ready output.
Bank feeds, card exports, invoice PDFs, and ERP extracts often arrive in formats that don't match each other. Before categorisation can happen, data needs to be cleaned and unified. We handle the messy middle - parsing, deduplication, currency conversion, and merchant name standardisation - so the categorisation engine receives consistent input.
Data pipelineGeneric ML models trained on public datasets often misclassify industry-specific spend. Our system is configured against your actual GL codes - not a default taxonomy. It learns from your historical decisions, flags ambiguous transactions for human review, and applies confidence scores so your team knows exactly where to focus their attention.
AI categorisationCategorised data only has value when it lands in the right place. We deliver structured outputs formatted for your accounting system - whether that's Xero, NetSuite, Sage, or a custom ERP. Scheduled exports, webhook delivery, and API access are all available depending on what your workflow requires.
Integration layer
The process is designed to require minimal ongoing input from your team once initial configuration is done. Most clients reach steady-state operation within the first two weeks.
We receive your chart of accounts and any existing categorisation rules or policies. A configuration session with your finance contact typically takes 90 minutes. We map your GL structure into the system and set confidence thresholds for auto-approval versus human review.
Sending us 3–6 months of previously categorised transactions lets the model calibrate to your specific vendor mix and spend patterns. This step is optional but meaningfully reduces the volume of flagged items in the first few cycles.
Transactions are processed in batches - daily, weekly, or at your preferred cadence. Items below the confidence threshold are queued in a review interface where your team makes final calls. Each decision feeds back into the model, narrowing the exception rate over time.
Approved categories are exported in your required format and pushed to your ERP or accounting tool. A monthly summary flags any patterns worth reviewing - new vendors appearing frequently, categories with unusual variance, or rule conflicts that developed during the period.
Most off-the-shelf categorisation tools use a shared model. That means a software company's SaaS subscriptions get treated the same way as a logistics firm's fuel costs. The distinction matters for reporting accuracy, tax treatment, and budget variance analysis.
Our approach keeps your configuration separate and your historical decisions private. The model improves only from your own corrections, which means it gets better at your specific edge cases rather than drifting toward generic averages.