Invoice Extraction for Complex PO Data
Checks each invoice against POs and past records to spot duplicates early and reduce back-and-forth.
Key Features
Multi-Model Approach for Field Extraction
The Co-pilot uses a mix of AI models to read and pull out key details from all kinds of invoice formats, no matter how they’re structured. Example: A vision-language model identifies structured fields like invoice numbers and dates, while an LLM extracts unstructured text such as payment instructions.
Pre-Training on 35 Million Invoice Fields
Trained on millions of real-world invoices, the system reliably handles a wide range of formats, vendors, and industry-specific layouts.
Field-Specific Model Optimization
Different types of data, like numbers, line items, and amounts, are handled by dedicated models to ensure each field is filled in accurately.
Chain-of-Thought Reasoning
Checks if related fields, like quantity, unit price, and totals, make sense together, helping catch errors and keep the data consistent.
Line-Item Parsing with Spatial Intelligence
Uses smart layout detection and table understanding to correctly capture detailed line items from multi-line invoices.
Contextual Validation Using Correlated Fields
The Co-pilot looks at how fields connect, like PO numbers and totals to confirm the data is consistent and accurate. Example: Cross-validating "Total Amount" against the sum of line-item totals and tax fields.
Payment Terms and Instructions Extraction
The Co-pilot reads and extracts key details like due dates, bank info, and payment instructions to keep transactions smooth and accurate. Example: Extracting "Net 30" payment terms and "Bank Account #12345" from invoice footnotes.
Error Correction Through Ensemble Learning
The Co-pilot checks results from different AI models and picks the most reliable answer, helping reduce mistakes in data extraction. Example: If two models predict slightly different invoice numbers, the ensemble mechanism selects the most likely match.
Handling of Semi-Structured and Unstructured Data
The Co-pilot can read non-standard invoices like PDFs and scans and still pull out the key details accurately. Example: Extracting handwritten totals from scanned invoices or detecting terms buried in unstructured notes.
Dynamic Adaptation to Domain-Specific Fields
The Co-pilot adjusts to different industries by learning unique invoice fields, ensuring accurate extraction for specific business needs. Example: Extracting "Lot Number" for pharmaceutical invoices or "Vehicle Identification Number (VIN)" for automobile-related invoices.
KEY BENEFITS
Achieve 80 % straight-through invoice processing as AI discovers, extracts, validates, matches, GL-codes, and posts to your ERP—shrinking manual effort, accelerating approvals, and boosting accuracy, compliance, and cost efficiency.
80%
Invoice processing cost
AI achieves up to 80% straight-through processing of invoices, freeing up staff bandwidth by 80%. Retained staff is empowered by Co-pilot with pin-pointed reasons to take quick decisions on business exceptions reported.
<1 min
Invoice processing time
Co-pilot reduces invoice processing time from an industry average of 11 days to less than one minute due to STP achieved through AI.
Human errors
Vendor satisfaction
Duplication & frauds
Auditability
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.
FAQs: Extraction
How does Hyperbots adapt to industry-specific fields?
The Co-pilot fine-tunes models for domain-specific needs, such as extracting "Lot Number" for pharmaceutical invoices or "VIN" for automotive invoices, ensuring relevance for various industries.
Can Hyperbots process semi-structured and unstructured data?
Yes, Hyperbots handles semi-structured formats like PDFs and scans, extracting key fields from unstructured notes or handwritten totals in scanned invoices.
How does Hyperbots correct errors in field extraction?
The Co-pilot uses ensemble learning to combine predictions from multiple models, selecting the most confident and accurate result to minimize errors in extraction.
Can Hyperbots extract payment terms and banking instructions accurately?
Yes, dedicated models are fine-tuned to identify and interpret fields like "Net 30" payment terms or banking details such as account numbers and remittance advice.
How is Hyperbots' extraction the best in the world, and how is it benchmarked?
Hyperbots leverages pre-training on millions of fields, specialized models, and ensemble learning. It benchmarks its performance against industry-standard datasets and real-world documents, consistently outperforming competitors in accuracy and efficiency. Contextual validation cross-checks correlated fields, such as ensuring "Total Amount" equals the sum of line-item totals and tax, preventing logical inconsistencies in extracted data.
What is the accuracy of Hyperbots' extraction?
Hyperbots achieves 99.8% accuracy in field extraction and ensures 100% reliability through contextual validation and augmentation for deployed agents.
How does the Co-pilot handle complex line-item parsing?
Hyperbots uses spatial intelligence to map details like "Item Code," "Quantity," and "Unit Price" accurately from tabular data, even in multi-line entries, ensuring reliable extraction.
What is chain-of-thought reasoning, and how does it help?
Chain-of-thought reasoning enhances contextual understanding by analyzing related fields together, such as linking payment terms with due dates and discount conditions for logical extraction.
Why does Hyperbots use field-specific models?
Different field categories are optimized with specialized models for tasks like numerical recognition for "amount fields" and table parsing for "line items," ensuring precise extraction for each data type.
What is the multi-model approach for field extraction in Hyperbots Co-pilot?
Hyperbots employs vision-language models (VLMs) and large language models (LLMs) to extract structured fields like invoice numbers and unstructured fields like payment instructions, ensuring accuracy across diverse invoice formats. Pre-training on a massive dataset ensures robust generalization, enabling the Co-pilot to accurately extract fields from invoices across industries, such as healthcare vendor details or retail item descriptions.