Extraction of Origin and Destination Addresses
Using a multi-model approach and pre-training on 35 million invoice fields, Hyperbots Sales Tax Verification Co-pilot accurately extracts both structured and unstructured data from diverse invoices.
Key Features
Multi-model field extraction
Combines vision-language models for structured fields with LLMs for unstructured text & layout models for understanding layouts like tables
Pre-training on 35 million invoice fields
Extensive training on a large, annotated dataset ensures robust performance across diverse invoice formats and industries.
Field-specific model optimization
Specialized models handle different data types (e.g., numerical fields, line items), maximizing accuracy for each category.
Chain-of-thought reasoning
Uses contextual logic to ensure consistent interpretation across related fields and data points.
Line-item parsing with spatial intelligence
Employs table-parsing and positional analysis to accurately map multi-line invoice details.
Intelligent line-item validation
Verifies the co-relationship between quantity, unit price, extended amount, net amount, gross amount, and tax, ensuring precise calculations and compliance.
Sales tax compliance
Co-pilot verifies applicable sales tax for every invoice and line item automatically
Tax underpayment
Co-pilots acts as a preventive methods against over or under charged sale tax by vendors
Auditability
Human Errors
Reputation
VALUE PROPOSITION
Why Hyperbots Sales
Tax-Verification Co-Pilot?
Hyperbots Sales Tax Verification Co-pilot automates tax validation for invoices, leveraging AI to ensure compliance with regional tax laws and eliminating errors, reducing manual effort and financial risks.
Before and After Hyperbots Sales Tax Verification Co-Pilot
FAQs: Extraction of Origin and Destination Addresses
How does the Hyperbots Co-pilot extract and validate origin and destination addresses?
Hyperbots combines vision-language models to detect address fields from structured elements (e.g., labels, tables) and LLMs to interpret unstructured text. It then cross-references key address components—such as street, city, state, and ZIP code—to verify completeness and consistency.
Can Hyperbots identify addresses if they appear in multiple sections or across different invoice pages?
Yes. Hyperbots’ advanced parsing algorithms and spatial intelligence allow it to locate addresses even when they are split across various sections or pages. It systematically segments invoice data to accurately capture each address component, regardless of format or layout.
Does the Hyperbots Co-pilot convert addresses into structured fields?
Absolutely. Once addresses are identified, the system organizes them into structured data fields (e.g., “Street Address,” “City,” “State,” “ZIP Code”) for easy validation, downstream processing, and integration with tax calculation systems.
What validations does it perform?
The Co-pilot checks for address completeness and consistency—ensuring fields like street name, ZIP code, and state match valid geographic records. It also verifies that the origin and destination addresses align with any shipping details, billing terms, or jurisdictional requirements.
Why is extraction and validation of origin and destination addresses important for sales tax verification?
Accurate tax calculations often depend on both the shipping point (origin) and the delivery point (destination). Verifying addresses helps ensure the correct tax jurisdictions are applied, reducing errors, minimizing compliance risks, and preventing under- or over-payment of taxes.
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.
