PR/PO Field Extraction from Contracts
Uses Agentic AI to capture vendor details, payment terms, and delivery dates.
Key Features
Multi-model field extraction
Uses different models together, one for reading structured fields, one for understanding layout, and another for text, to extract information accurately, even from complex tables.
Pre-training on millions of documents
Built on a large dataset of real-world documents, enabling consistent and accurate performance across a wide range of contract types and industries.
Field-specific model optimization
Uses dedicated models for different kinds of data, like numbers, amounts, or line items, to improve how accurately each type is extracted.
Chain-of-thought reasoning
Applies step-by-step reasoning to understand how related fields connect, ensuring consistent and accurate interpretation across the document.
Line-item parsing with spatial intelligence
Uses spatial cues and table structure to accurately capture detailed line items, even when information spans across multiple rows.
Intelligent line-item validation
Checks that quantities, prices, totals, and taxes align correctly, helping ensure accurate calculations and contract compliance.
KEY BENEFITS
Procurement Co-Pilot shrinks cycle times from request to purchase order, freeing teams from manual effort, delivering instant ERP-synced approvals, and giving finance teams real-time spend visibility for compliant, cost-effective purchasing.
80%
PO creation & dispatch time
Converts approved PR into PO automatically based on the company's templates. It can send POs automatically to vendors based on configurations.
5min
PR creation time
Co-pilot auto-fills most of the complex forms in ERPs and procurement systems by reducing the effort to 5 minutes
Auditability
Human Errors
Approvals
PO Closing & Other KPIs
Before and After Hyperbots PR/PO Co-Pilot
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 of PR Fields from Contract
What fields does the PR/PO Co-Pilot extract from contracts or SOWs?
The PR/PO Co-Pilot extracts essential fields such as vendor information, line item details, payment terms, delivery dates, contract terms, and scope of work from contracts and Statements of Work (SOWs). This ensures all critical procurement data is accurately captured.
What time savings does the PR/PO Co-Pilot achieve by extracting these fields?
By automating the extraction of key fields, the Co-Pilot significantly reduces manual data entry time, typically saving up to 80% of the time spent on processing purchase requisitions and orders. This allows procurement teams to focus on more strategic tasks.
What technology does the PR/PO Co-Pilot use to extract these fields?
The Co-Pilot utilizes advanced extraction technologies, including vision-language models, large language models (LLMs), and layout understanding algorithms. These state-of-the-art models enable accurate extraction from diverse document formats, such as structured forms, free-form text, and complex table layouts.
Does the PR/PO Co-Pilot also perform validation?
Yes, the Co-Pilot not only extracts data but also validates the extracted fields against predefined rules and existing records. It ensures data accuracy by cross-referencing vendor details, verifying line items, and confirming payment terms, thereby minimizing errors and maintaining compliance.
Does the PR/PO Co-Pilot highlight where human input is necessary based on confidence?
Absolutely. The Co-Pilot assigns confidence scores to each extracted field and flags any low-confidence entries for human review. This ensures that critical decisions involve human oversight when the system's certainty is insufficient, maintaining high data integrity.
Does the PR/PO Co-Pilot perform inference-time learning?
Yes, the Co-Pilot incorporates inference-time learning, allowing it to adapt and improve its extraction and validation processes based on real-time feedback and new data. This continuous learning capability enhances accuracy and performance over time, ensuring the system remains up-to-date with evolving procurement needs.
Abbott
AbbVie
Amazon
Apple
Arrow Electronics
AT&T
AutoNation
Bank of America
Berkshire Hathaway (GEICO)
CBRE
Cigna Group
Cisco Systems
Citigroup
Costco
Dell Technologies
Dollar Tree
Duke Energy
Eli Lilly
ExxonMobil
FedEx
Goldman Sachs
Honeywell
HP / Hewlett Packard Enterprise
IBM
Jewel-Osco (Albertsons)
JLL
Johnson & Johnson
Liberty Mutual
Lowe's
Marathon Petroleum
Marriott International
Mastercard
McKesson
Medline
MetLife
PepsiCo
Pfizer
Phillips 66
Progressive
Southwest Airlines
Starbucks
Stryker
The Home Depot
Travelers
Tyson Foods
U.S. Bancorp
Valero Energy
Walmart
Warner Bros. Discovery
3M
Allstate
American Airlines
American Express
Applied Materials
Best Buy
Boeing
Capital One
Cardinal Health
Cencora
Charter Communications (Spectrum)
Chevron
Comcast
Cummins
CVS Health
Dollar General
Ford Motor Company
General Dynamics IT
Google (Alphabet)
Humana
International Paper
JPMorgan Chase
Kraft Heinz
Kroger
Marsh McLennan
McDonald's
Merck & Co.
Microsoft
Mondelez
Nationwide
PayPal
Prudential Financial
Publix
Southern Company
State Farm
Sysco
Target
The Hartford
The Walt Disney Company
Thermo Fisher Scientific
Uber
UNFI
United Airlines
UnitedHealth Group (incl. Optum)
UPS
US Foods
Verizon
Waste Management
Wells Fargo