Classifying Tax Categories in Line Items

Applies Agentic AI to match invoice data with relevant tax rules using context-aware classifiers and confidence scoring, surfacing edge cases for review.

Key Features

"Tags line items using predefined product and service taxonomies"

Automated taxonomy recognition

Automatically labels each line item, like goods, services, or tax-exempt, based on a standard category list.

"Trained on invoice data to identify keywords and pricing for categorization"

Machine learning classifiers

Learns from thousands of invoices to spot key terms, item details, and pricing patterns, helping categorize line items accurately.

"Uses buyer-vendor history and context to classify vague item descriptions"

Context-aware matching

When item descriptions are unclear, the system looks at past buyer-vendor interactions and industry context to help make accurate classifications.

"Assigns confidence scores and flags low-confidence items for review"

Confidence scoring & auditing

Each classification comes with a confidence score. If the score is low, the item is flagged for someone to double-check.

Continuously retrains on user feedback, refining classifications over time.

Adaptive learning loop

The system learns from user corrections and feedback, improving how it classifies items over time.

KEY BENEFITS

Hyperbots Sales Tax Verification Co-Pilot locks in sales-tax compliance on every invoice, shutting down tax underpayment penalties before they start. A built-in, time-stamped trail delivers effortless auditability, while near-zero human errors protect margins and, ultimately, your company’s reputation for accuracy and integrity.

Sales tax compliance

Co-pilot verifies applicable sales tax for every invoice and line item automatically

Tax underpayment

Co-pilot acts as a preventive method against over or under-charged sales tax by vendors

Auditability

Human errors

Reputation


Before and After Hyperbots Sales Tax Verification Co-Pilot

Why Hyperbots Agentic AI Platform?

Why choose hyperbots agentic AI: finance-first, accurate, adaptable AI

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.

FAQs: Tax Category Classification

How does Automated Taxonomy Recognition classify line items?

It uses a predefined taxonomy of products/services to tag each line item (e.g., “Goods,” “Services,” “Exempt”), ensuring correct tax rates or exemptions.

Where does the Hyperbots Co-pilot pick up tax categories from?

The Co-pilot references a structured taxonomy maintained by the Agentic Platform, which includes product/service categories along with their relevant tax rates or exemptions.

Why do Machine Learning Classifiers improve over time?

They continuously learn from new invoices and user corrections, refining their ability to identify category-specific keywords and patterns.

What is Context-Aware Matching?

It considers buyer-vendor history, industry context, and invoice templates to reduce misclassifications when item names are ambiguous.

How does Confidence Scoring and Auditing help?

Each classification has a confidence score; low-scoring items are flagged for review, creating a feedback loop for ongoing accuracy improvements.

Can Hyperbots integrate with other systems after categorization?

Yes. Correct tax rules are applied, and the categorized data seamlessly connects to finance, ERP, or compliance systems for reporting and audits.

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!