Key Features
Comprehensive Field Coverage
The Co-pilot matches invoices with POs and GRNs across 140+ fields from numbers and dates to text and descriptions for accurate results every time. Example: Matching fields like "Unit Price," "Quantity," "Total Amount," "Vendor Name," and "Payment Terms."
Advanced Numeric Matching with Mathematical Reasoning
The Co-pilot uses math reasoning to match numbers accurately, even when units or formats don’t line up perfectly. Example: Automatically converting quantities or amounts (e.g., millions vs. thousands) between an invoice and a PO for consistency.
Expression-Based Matching for Complex Terms
The Co-pilot can understand and match complex phrases or calculations using smart evaluators built to handle tricky cases. Example: Matching payment terms like "1/10 Net 30" in PO with "1% discount applies if payment is made within 10 days", fromthe invoice’s payment terms.
Descriptive Field Matching Using Language Models
The Co-pilot matches vendor names and item descriptions, even when they’re worded differently across documents. Example: Matching "ABC Corporation" with "ABC Corp." or mapping "Laptop - Model X" to "Laptop X-Series."
Reasoning Models for Anomaly Detection
If something doesn’t match, the Co-pilot flags it and explains why, making it easy for teams to review and fix issues. Example: Explaining that the "Quantity" in the GRN does not align with the "Quantity" in the invoice due to partial delivery.
Pre-Training on Millions of Invoice Fields
Trained on millions of invoice fields, the Co-pilot adapts easily to different formats and industries for more accurate results. Example: Recognizing and accurately matching unique terms or formats used in manufacturing or healthcare invoices.
Dynamic Multi-Model Matching
The Co-pilot combines math, logic, and language skills to accurately match fields—even in complex or varied documents. Example: Simultaneously verifying numeric totals, payment expressions, and item descriptions in a single matching process.
Missing Documents
The Co-pilot flags missing POs or GRNs for review, and automatically resumes matching once they’re added to the ERP.
User Transparency with Matching Results
The Co-pilot explains why a match failed, like missing or mismatched fields, so teams can quickly understand and fix issues with less effort. Example: Highlighting a mismatch where the GRN shows a delivered quantity of 100 units, but the invoice specifies 120 units. By integrating reasoning models, expression evaluators, and language models pre-trained on millions of fields, Hyperbots achieves unmatched 3-way matching capabilities, enabling precise automation and reducing human effort in handling exceptions.
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.
Ready to take the next steps?
Book a demo with one of our Financial Technology Consultants to get started!