Accurate Invoice Data for Straight-Through Processing
Corrects missing or inaccurate invoice data to support consistent straight-through processing with minimal manual effort.
Key Features
Mathematical Field Augmentation
If any totals or amounts don’t add up during validation, the system recalculates and updates them automatically, reducing manual fixes and improving accuracy. Example: If the total amount and quantity are correct, but the unit price is wrong, it recalculates and augments the unit price to ensure consistency.
Inference-Based Field Completion
When a field is left blank, the system fills it in by using related information, so even incomplete invoices can be processed without delays. Example: If the due date is missing, it is calculated based on the invoice date and payment terms.
Contextual Data Augmentation
If vendor details are missing or incorrect, the system looks at related documents like purchase orders to complete the information, so nothing holds up processing. Example: An incomplete vendor address on the invoice is replaced with the verified address from the associated PO.
Tax and Discount Corrections
Automatically updates tax and discount fields based on business rules or historical vendor records, ensuring consistency and accuracy. Example: An incorrect tax calculation on the invoice is updated to match the applicable rate for the transaction.
Payment Instruction Augmentation
Completes partial payment information by referencing vendor records or past invoices, reducing delays in processing. Example: Missing bank account details are retrieved and augmented from the vendor's master record.
Line Item-Level Augmentation
Fills in or fixes missing details at the line-item level, such as descriptions, quantities, or rates, so records stay complete and accurate. Example: A missing item description is inferred based on the PO reference and updated on the invoice.
Error Reduction and Rejection Avoidance
Catches and fixes issues early, before they cause problems during invoice matching, helping reduce rejections and delays. Example: Augmenting a missing due date avoids rejection by agentic platform dueing 2-way/3-way matching.
Higher STP Rates
Automatic field completion reduces the need for manual edits, leading to smoother processing and higher straight-through processing (STP) rates. Example: Correcting a minor discrepancy in payment terms ensures the invoice is processed automatically without delays.
Improved Data Consistency
Keeps information aligned across related documents, like purchase orders, GRNs, and invoices, by filling in missing or incorrect values where needed. Example: A mismatched delivery date on the invoice is aligned with the GRN date.
Transparency in Augmentation
When invoice fields are updated or filled in, the system shows what was changed and the logic behind it. This makes the process clear and helps teams review changes with confidence. Example: A report highlights that the unit price was recalculated due to a mismatch with the total amount and quantity.
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: Augmentation
What is the purpose of augmentation in Hyperbots Co-pilots?
The platform enhances invoice accuracy by correcting errors and filling in missing or incomplete fields, reducing manual intervention and achieving higher STP rates.
How does mathematical field augmentation work?
Hyperbots recalculates and updates incorrect mathematical fields like total amounts, unit prices, and quantities to ensure consistency. Example: If the total amount is correct but the unit price is wrong, the platform recalculates and updates it.
How does inference-based field completion improve processing?
Missing fields are logically inferred using related data, such as calculating a missing due date based on the invoice date and payment terms.
Can Hyperbots augment missing vendor information?
Yes, the platform retrieves vendor details like addresses and bank account information from linked documents like POs or vendor master records.
How does Hyperbots handle incorrect tax and discount calculations?
The platform adjusts tax amounts and discounts using predefined business rules or using its tax verification co-pilot. Example: Updating a tax field to match the applicable rate for a specific transaction.
Does Hyperbots improve line-item accuracy?
Yes, the platform augments missing or incorrect line-item details like descriptions, quantities, and unit prices based on references such as POs. Example: Adding a missing item description using PO data.
How does augmentation prevent invoice rejections?
By correcting and augmenting data before the validation stage, Hyperbots reduces rejections during matching processes like 2-way or 3-way matching. Example: Adding a missing due date avoids unnecessary rejection.
How does augmentation improve data consistency?
Hyperbots ensures alignment across related documents, such as matching invoice delivery dates with GRN dates to maintain consistency.
How does augmentation contribute to higher STP rates?
By automating corrections and augmentations, the platform minimizes human intervention, enabling seamless invoice processing and maximizing efficiency.
What transparency does Hyperbots provide for augmented fields?
The platform explains every augmented field, detailing the reason for the change, giving users confidence in its accuracy. Example: Highlighting that a recalculated unit price ensures alignment with total and quantity.