Invoice GL Coding Using Learned Behaviors
GL codes are recommended based on patterns in historical invoice data and previous user selections, reducing manual coding effort.
Key Features
AI-Driven GL Recommender
The GL recommender suggests codes based on past data like vendors, items, or expense types, helping maintain consistency and save time.
Historical Data Learning
During setup, the system reviews past invoices and GL data to learn common coding patterns and improve future suggestions.
Vendor and Expense-Based Logic
GL codes are assigned by looking at past vendor data and expense types, helping keep entries consistent with accounting rules. Example: Vendor "ABC Supplies" is consistently coded to "Office Supplies Expense."
Item and Category Intelligence
Uses both item details and categories to suggest the right GL codes for each line item on an invoice. Example: A line item for "Printer Ink" is coded to "Office Consumables."
Self-Learning from Human Corrections
The system improves over time by learning from corrections made during non-PO invoice approvals.
Context-Aware Recommendations
The Co-pilot suggests GL codes by also looking at things like payment terms, descriptions, and PO details for better accuracy.
Adaptive and Scalable
The GL recommender adjusts over time, learning from new vendors, categories, and changes in business rules. Example: A new vendor added to the system automatically integrates into the AI’s learning model.
Transparency and Human Validation
The Co-pilot shows why a GL code was suggested, so users can review, confirm, or make changes as needed, keeping things accurate and flexible.
KEY BENEFITS
Achieve 80 % straight-through invoice processing as AI discovers, extracts, validates, matches, GL-codes, and posts to your ERP—shrinking manual effort, accelerating approvals, and boosting accuracy, compliance, and cost efficiency.
80%
Invoice processing cost
AI achieves up to 80% straight-through processing of invoices, freeing up staff bandwidth by 80%. Retained staff is empowered by Co-pilot with pin-pointed reasons to take quick decisions on business exceptions reported.
<1 min
Invoice processing time
Co-pilot reduces invoice processing time from an industry average of 11 days to less than one minute due to STP achieved through AI.
Human errors
Vendor satisfaction
Duplication & frauds
Auditability
Why Hyperbots Agentic AI Platform?
Finance specific
Hyperbots Agentic AI platform specializes exclusively in finance and accounting intelligence, leveraging millions of data points from invoices, statements, contracts, and other financial documents. No other platform has such large pretrained models on F&A data.
Best-in-class accuracy
Hyperbots achieves 99.8% accuracy in converting unstructured data to structured fields through a multimodal MOE model integrating LLMs, VLMs, and layout models. With contextual validation and augmentations, the platform ensures 100% accuracy for deployed agents.
Synthesis of unstructured and strutured finance data
Hyperbots agents emulate finance professionals to autonomously perform F&A tasks by reading and writing data like COA, expenses, and vendor masters from core accounting systems and integrating it with unstructured data from financial documents such as invoices, POs, and contracts.
Pre-trained agents with state of the art models
Hyperbots' Agentic platform, pre-trained on millions of financial documents like invoices, bills, statements, and contracts, ensures seamless integration, high accuracy, and adaptability to any accounting content, form, layout, or size from day one.
Company specific inference time learning
Hyperbots' Agentic platform employs state-of-the-art Auto ML pipelines with techniques like reinforcement learning to enable inference-time learning for tasks such as GL recommendation and cash outflow forecasting, ensuring continuous improvement and adaptability.
