Key Features
Vendor-Based Rules
Matching rules can be set per vendor, defaulting to 3-way matching, with exceptions for vendors that require 2-way or no matching based on specific agreements or needs.
Expense Category-Based Rules
Matching rules can vary by expense type, for example, 3-way for hardware, 2-way for legal services, and no matching for utilities, ensuring appropriate checks for each category.
Customizable to Organizational Needs
Matching strategies can be tailored to fit specific workflows, policies, and scenarios, offering flexibility to align with each organization’s operational and compliance needs.
Dynamic Exception Handling
Exceptions to standard rules can be set for specific vendors or expenses, allowing flexible configurations without impacting the broader workflow.
Streamlined Process Efficiency
Automated matching rule setup helps speed up processing and reduces manual work for transactions that don’t need strict checks.
Scalable and Adaptable
Designed to scale with new vendors or expense types while keeping control over matching workflows, ensuring efficient invoice processing without compromising compliance or accuracy.
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!