Validation
The Hyperbots agentic platform performs advanced validations using mathematical reasoning, language models, and anomaly detection to ensure data accuracy.
Key Features
Mathematical reasoning for data accuracy
Uses algorithms to verify numerical fields like quantities and totals, ensuring all calculations are correct and consistent.
Anomaly detection for error identification
Identifies unusual patterns or discrepancies in the data, preventing errors from affecting the procurement process.
Transparent validation explanations
Provides clear explanations for each validation result, helping users understand why data was accepted or flagged.
Automated consistency checks
Ensures related fields such as vendor details and payment terms align logically, maintaining data integrity across forms.
Enhanced reliability and efficiency in field extraction
Combines mathematical reasoning, language models, and anomaly detection to streamline data extraction and validation, reducing manual efforts and increasing accuracy.
80%
PO creation & dispatch time
Converts approved PR into PO automatically based on the company's templates. It can send POs automatically to vendors based on configurations.
5min
PR creation time
Co-pilot auto-fills most of the complex forms in ERPs and procurement systems by reducing the effort to 5 minutes
Auditability
Human Errors
Approvals
PO Closing & Other KPIs
VALUE PROPOSITION
Why Hyperbots Procurement
Co-Pilot?
Hyperbots PR/PO Co-pilot automates purchase requisition and order processes, including PR creation, approval workflows, and PO dispatch, ensuring efficiency, compliance, and seamless integration with ERP systems.
Before and After Hyperbots PR/PO Co-Pilot
FAQs: Validation
How does the PR/PO Co-Pilot ensure numerical data accuracy?
The Co-Pilot uses mathematical reasoning to verify numerical fields such as quantities, unit prices, and totals. It ensures all calculations are correct and consistent, minimizing errors in financial data and line item details.
How does the Co-Pilot detect and handle data anomalies?
The Co-Pilot employs anomaly detection techniques to identify unusual patterns or discrepancies in the extracted data. By spotting outliers early, it prevents errors from impacting the procurement process and ensures data integrity.
What are Transparent Validation Explanations in the Co-Pilot?
For every validation check, the Co-Pilot provides clear explanations of the outcomes. This transparency helps users understand why certain data points were accepted or flagged, facilitating quicker issue resolution and building trust in the system.
How does the Co-Pilot maintain consistency across related fields?
The Co-Pilot performs automated consistency checks to ensure that related fields, such as vendor details and payment terms, align logically. This maintains data integrity across all forms and reduces the risk of conflicting information.
In what ways does the Co-Pilot enhance reliability and efficiency in data extraction?
By combining mathematical reasoning, anomaly detection, and automated consistency checks, the Co-Pilot streamlines the data extraction and validation process. This reduces the need for manual verification, increases accuracy, and enhances overall workflow efficiency.
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!