Extraction and Validation of Line Item Details

Using a multi-model approach and pre-training on 35 million invoice fields, Hyperbots Sales Tax Verification Co-Pilot accurately extracts 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 and Validation of Line Item Details

How does the Hyperbots Co-pilot extract and validate line items?

Hyperbots leverages multi-model AI—combining vision-language models to detect structured elements (like tables and columns) and large language models (LLMs) to interpret unstructured text. It extracts each line item’s details (e.g., description, quantity, unit price) and validates them by cross-referencing fields such as net amount, tax amount, and other invoice data.

Can Hyperbots identify line items if there are hundreds spread across multiple tables in complex invoices?

Yes. Hyperbots’ advanced table-parsing algorithms and spatial intelligence enable it to handle multiple tables, nested formats, and high-volume line items. It accurately segments invoice sections and reliably captures each individual entry, regardless of complexity or length.

Does the Hyperbots Co-pilot convert line item information into structured fields?

Absolutely. Once the system has extracted line item details, it organizes them into structured data fields (e.g., item code, quantity, unit price, total) for easy downstream processing, integration with accounting systems, and accurate tax computation.

What validations does it perform?

The Co-pilot checks for consistency between related fields (e.g., quantity × unit price = extended amount, correct tax rates applied, net amount plus tax = gross amount). It flags discrepancies, suggests corrections, and ensures compliance with applicable tax regulations or contract terms.

Why is extraction and validation of line items important for sales tax verification?

Accurate sales tax calculation relies on a precise breakdown of each line item’s taxable status and amount. Validating line items reduces errors in tax reporting, minimizes compliance risks, and helps organizations avoid over- or under-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.

Designed by CFOs for CFOs

We worked with several CFOs to solve the right problems.

Hear what they have to say!

Designed by CFOs for CFOs

We worked with several CFOs to solve the right problems.

Hear what they have to say!

Ready to take the next steps?

Book a demo with one of our Financial Technology Consultants to get started!

Ready to take the next steps?

Book a demo with one of our Financial Technology Consultants to get started!