Matching POs and GRNs with Agentic AI
Performs detailed field-level matching using numeric logic and language models to support invoice matching and accurate accruals.
Key Features
Comprehensive field coverage
Matches POs and GRNs across 100+ fields, including numbers, text, and descriptions, for flexible and accurate 2-way matching.
Advanced numeric matching with mathematical reasoning
Accurately matches numbers across documents, even with different units or formats, to ensure consistency and precision.
Expression-based matching for complex terms
Uses expression evaluators to match complex payment terms accurately between POs and GRNs.
Descriptive field matching using language models
Matches fields like vendor names and item descriptions, even when phrasing varies, to improve accuracy.
Reasoning models for anomaly detection
Flags mismatches and explains why a match failed, making it easier for teams to review and resolve issues.
Pre-training on millions of invoice fields
Trained on large, diverse datasets to ensure high accuracy and adaptability across different industries.
Dynamic multi-model matching
Combines numeric logic, expression evaluation, and language understanding to match fields accurately across documents.
Missing documents
Flags missing POs or GRNs for review and automatically re-tries matching once the documents are available in the ERP.
User transparency with matching results
Provides clear insights into match failures, helping users understand issues and take corrective action.
KEY BENEFITS
Accruals Co-Pilot automates detection, posting, and reversal, wiping out month-end busywork; machine learning sharpens forecasts and audit trails; policy-aware configuration snaps into any ERP—cutting errors, risk, and workload in one stroke.
80%
Accrual processing cost
Co-pilot reports all accrued expenses using AI eliminating the need for manual accruals completely
<5%
Variance in accured Vs actual costs
Co-pilot identifies all expenses comprehensively for all type of scenarios through data using AI.
Human Errors
Accrual reversal
Month end closing pressure
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.
FAQs: 2-Way Matching
How does the Accruals Co-Pilot handle a wide range of fields during the matching process?
It supports over 100 fields, including numeric, textual, and descriptive elements, allowing flexible and accurate two-way matching between purchase orders (POs) and goods receipt notes (GRNs).
Can the Co-Pilot handle inconsistencies in numeric data, like different units or formats?
Yes. Its advanced numeric reasoning can normalize and compare values across varying units or formats, ensuring precise matches even when the data isn’t perfectly aligned.
How does the Co-Pilot manage complex payment terms found in POs or GRNs?
It uses dedicated expression evaluators to interpret and match complex terms, ensuring that nuanced contractual details are properly accounted for during the matching process.
Can the Co-Pilot accurately match descriptive fields like vendor names or product descriptions?
Absolutely. By leveraging language models, it can interpret variations in phrasing, spelling, or formatting, ensuring that descriptive fields are aligned correctly.
What if there are discrepancies or anomalies detected during the matching process?
The Co-Pilot employs reasoning models to highlight discrepancies and provide detailed reasons for failed matches, enabling effective human review and corrective action.
How does the Co-Pilot maintain high accuracy across diverse industries and document types?
It’s pre-trained on millions of invoice fields from multiple sectors, ensuring adaptability and consistently high accuracy, regardless of industry-specific variations.
How does the system handle missing documents such as a PO or GRN?
If a required document is missing, the Co-Pilot flags it for human intervention and automatically re-initiates the matching process once the missing document is added to the ERP. This ensures no accruals are overlooked due to missing data.
