Field-Level Data Validation for Invoices
Agentic AI checks invoice fields against PO data, flags duplicates and inconsistencies, and helps reduce manual steps in the validation process.
Key Features
Mathematical Reasoning for Amount Relationships
The Co-pilot makes sure numbers on the invoice make sense, like checking that totals match item prices, taxes, and quantities. Example: Ensures that Net Amount = Gross Amount - Tax and Total Amount = Quantity × Unit Price.
Language-Based Vendor and Payment Validations
The Co-pilot uses large language model’s understanding to check vendor names, bank details, and payment info,so everything lines up correctly. Example: Cross-checks vendor names, banking details, and payment terms against the vendor master to identify discrepancies.
Date Format Detection and Validation
The Co-pilot understands different date formats and makes sure they’re correct based on how dates are used in the invoice. Example: Identifies an incorrectly formatted due date (e.g., MM/DD/YYYY instead of DD/MM/YYYY) and corrects it.
Anomaly Detection in Invoice Data
The Co-pilot spots unusual entries, like duplicates, wrong totals, or odd tax rates, so issues can be fixed before they cause trouble. Example: Flags an invoice with a tax rate significantly different from historical averages for the same vendor.
Validation Transparency with Reasoning Explanations
For every check, the Co-pilot shows its reasoning, making it easy to review and trust how decisions were made. Example: If a tax calculation fails, the Co-pilot explains that the discrepancy arose due to a mismatch between the invoice's declared tax rate and the standard rate.
Dynamic Validation Rules
The Co-pilot adjusts its checks based on the invoice type, vendor, or industry, so validations stay accurate and relevant. Example: Enforces stricter validations for high-value invoices or those from new vendors.
Error Flagging and Reporting
The Co-pilot spots issues in invoices, flags them clearly, and organizes them into simple reports, making review and corrections easier. Example: If payment instructions are missing, the system highlights the issue and suggests corrections.
AI-Driven Workflow Optimization
The Co-pilot sorts validation results automatically, prioritizing what needs attention and guiding each invoice through the right steps with less manual effort. Example: Directs flagged invoices to the finance team for review while allowing validated invoices to proceed automatically.
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.
FAQs: Validation
How does the Co-pilot reduce manual intervention in validation?
By combining mathematical reasoning, anomaly detection, and contextual understanding, the Co-pilot automates validations, resolving most issues autonomously while only escalating complex errors to human users.
How does the Co-pilot optimize workflows based on validation results?
The Co-pilot automatically routes invoices with validation issues to the appropriate team while allowing error-free invoices to proceed in the workflow, prioritizing urgent issues for faster resolution.
What happens when validation errors occur?
Validation failures are flagged, categorized, and reported in detail for user review. For example, if payment instructions are incomplete, the Co-pilot highlights the issue and suggests corrections.
How does the Co-pilot adjust validation rules for different scenarios?
The Co-pilot dynamically adjusts validation rules based on factors like invoice type, vendor history, or industry-specific requirements. For example, it applies stricter checks for high-value invoices or new vendors.
What makes the Co-pilot's validation process transparent?
For every validation performed, the Co-pilot provides explanations for the outcome, such as why a tax calculation failed or a vendor name mismatch occurred, enhancing user confidence in its decisions.
How does the Co-pilot handle anomaly detection in invoices?
The Co-pilot leverages AI reasoning to identify anomalies like duplicate entries, mismatched amounts, or unusual tax rates. For instance, it flags invoices with tax rates significantly different from historical averages.
Can the Co-pilot detect and standardize different date formats?
Yes, the Co-pilot identifies various date formats (e.g., MM/DD/YYYY vs. DD/MM/YYYY) and standardizes them while validating their correctness in the context of invoice timelines, such as due dates and payment dates.
How does the Co-pilot validate vendor and payment information?
Using large language models, the Co-pilot cross-checks textual fields like vendor names, banking details, and payment terms against the vendor master, flagging any discrepancies for review.
How does the Co-pilot validate mathematical relationships between invoice fields?
The Co-pilot uses mathematical reasoning to ensure consistency across fields like gross amount, net amount, tax, and unit prices. For example, it confirms that Net Amount = Gross Amount - Tax and Total Amount = Quantity × Unit Price.
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!