GL Coding & Cost Allocation Automation
GL coding automation uses AI agents to assign general ledger codes from invoice content, historical patterns, and your cost center rules – and to catch miscodes before they post. Fragment runs it across every business unit and ERP, with no rip-and-replace.
Where the manual work comes from.
GL miscoding is one of the most expensive quiet problems in AP. Each miscoded invoice eventually resurfaces – as a budget variance, a journal correction, or a reconciliation at close.
Non-PO invoices defeat manual coding
Intercompany charges and complex multi-line invoices spanning cost centers are exactly where human coding accuracy falls apart.
Miscodes accumulate silently
Errors build up unnoticed through the month and surface at close as budget variances that need investigation.
Corrections consume the close
Accounting teams spend 20-30% of close cycle time on GL reclassification and correction entries.
Inconsistent coding breaks consolidation
Different business units code the same spend differently, making consolidated reporting unreliable without manual normalization.
How Fragment runs the workflow.
Fragment's agents code every invoice with full context, and get more accurate the longer they run.
1. Code each invoice from context
Agents read invoice content, historical coding patterns, PO data, and cost center rules to assign GL codes with high confidence.
2. Detect anomalies before they post
Unusual coding patterns are flagged in real time, before month-end instead of after.
3. Chase systematic errors upstream
The continual learning engine identifies recurring miscoding trends and corrects the upstream process that produces them.
4. Enforce standards across every ERP
Consistent coding rules apply across business units and systems, cutting the normalization work at consolidation.
The same workflow, with and without Fragment.
- Manual coding on non-PO and multi-line invoices
- Errors surface at close as budget variances
- 20-30% of close cycle time spent on corrections
- Coding standards drift across business units
- Consolidated reporting needs manual normalization
- Codes assigned from content, history, and rules
- Anomalies caught before posting
- 50-70% fewer correction entries at close
- One coding standard across units and ERPs
- Reporting that consolidates cleanly
What changes when agents take the volume.
- 90%+ auto-coding accuracy on first pass
- 50-70% reduction in month-end GL correction entries
- Anomalies caught before they post, instead of at close
- Consistent standards across business units and ERPs
- Less accounting labor absorbed by reclassification
Frequently asked questions.
What is GL coding automation?
GL coding automation uses AI agents to assign general ledger codes from invoice content, historical patterns, PO data, and cost center rules – reaching 90%+ first-pass accuracy, including on non-PO and multi-line invoices.
How does Fragment catch miscodes before they post?
Anomalous coding patterns are detected in real time and flagged before posting, replacing the month-end cycle of finding and correcting errors after they have distorted budgets.
Can it keep coding consistent across business units?
Yes. Agents enforce one coding standard across units and ERPs, which cuts the manual normalization that consolidated reporting usually requires.
Does Fragment replace our accounting system?
No. Fragment works on top of your existing ERP and AP stack, with no data migration and no rip-and-replace.
How long does deployment take?
Fragment connects to your existing systems through secure APIs and standard connectors, with no data migration. Typical time to first value is two to four weeks from kickoff to live auto-coding, with ROI visible within days of going live and accuracy improving as the learning engine sees more of your spend.
Deploy on your systems and automate this workflow.
Fragment deploys on top of the systems you already run, with no data migration, and expands across your source-to-pay workflows as the savings land.
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