Tax Category Classification
Hyperbots Co-pilot classifies line items into the correct tax category by using ML classifiers, context-aware matching, and confidence scoring that flags uncertainties for review.
Key Features
Automated taxonomy recognition
Uses a predefined product/service taxonomy to tag line items (e.g., goods, services, exempt).
Machine learning classifiers
Trained on large invoice datasets to identify keywords, descriptions, and pricing patterns for accurate categorization.
Context-aware matching
Considers buyer-vendor history and industry context to classify items when descriptions are vague.
Confidence scoring & auditing
Assigns a confidence level to each classification, flagging low-scoring items for manual review.
Adaptive learning loop
Continuously retrains on user feedback, refining classifications over time.
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: 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.
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.
