2-Way Matching
Hyperbots Co-Pilot offers comprehensive field coverage, leveraging numeric reasoning, expression evaluation, and language models to accurately match POs with GRNs across more than 100 fields.
Key Features
Comprehensive field coverage
Supports matching across 100+ fields, including numeric, textual, and descriptive fields, enabling flexible 2-way matching between POs and GRNs.
Advanced numeric matching with mathematical reasoning
Performs precise numeric matching, even when units or formats are inconsistent, ensuring accuracy across documents.
Expression-based matching for complex terms
Matches complex payment terms using dedicated expression evaluators to align with PO and GRNs.
Descriptive field matching using language models
Matches descriptive fields like vendor names or line-item descriptions, even with variations in phrasing.
Reasoning models for anomaly detection
Highlights discrepancies and provides detailed reasons for failed matches, enabling effective human review.
Pre-training on millions of invoice fields
Ensures high accuracy and adaptability across industries by leveraging vast datasets during model training.
Dynamic multi-model matching
Integrates mathematical reasoning, expression evaluation, and language models for comprehensive field matching.
Missing documents
Hyperbots co-pilot checks the missing PO or GRN, and flags that as a reason for human to take action. It autmatically re-initates the matching whenever missing
PO or GRN is made available within ERP.
User transparency with matching results
Delivers actionable insights into match failures, ensuring clear visibility and enabling corrective actions.This robust matching process ensures precise automation, reduces human effort, and enhances accruals discovery accuracy.
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
VALUE PROPOSITION
Why Hyperbots Accruals Co-Pilot
Hyperbots Accruals Co-pilot automates accrual identification, booking, and reversal processes with high configurability and accuracy, ensuring timely and compliant financial reporting while reducing manual effort and errors.
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.
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!