Human-in-the-loop
Hyperbots Co-pilots integrate humans into the loop by escalating exceptions, supporting approval workflows, providing actionable notifications, and learning from human feedback.
Key Features
Exception handling
Hyperbots Co-pilots flag exceptions during processing, enabling humans to review and resolve discrepancies efficiently.
Approval workflows
Co-pilots integrate seamlessly with human approval processes, routing invoices, payments, or other tasks to designated approvers based on company-defined thresholds.
Interactive notifications and insights
Detailed notifications and insights are provided to humans, highlighting task progress, completed actions, and unresolved issues for informed decision-making.
Learning from human actions
The Co-pilots incorporate feedback from human actions, such as corrections or overrides, to refine AI models and improve accuracy over time.
Comparison with competition
FAQs: Human-in-the-loop
Why is human in the loop necessary for Hyperbots Co-pilots?
Human in the loop is essential for handling exceptions, decision-making in ambiguous scenarios, or addressing tasks requiring judgment or domain expertise, ensuring accuracy and compliance.
Which humans are brought into the loop, and for what purpose?
Typically, finance staff such as AP clerks, accountants, controllers, or senior executives are involved to review flagged exceptions, approve payments, validate corrections, or manage escalations in workflows.
Is the level of involvement of humans configurable?
Yes, the level of human involvement is fully configurable based on business requirements. Companies can define when and how humans are engaged, such as thresholds for approvals or specific exceptions requiring manual intervention.
What are the modes using which humans are kept in the loop by Hyperbots Co-pilots?
Humans are kept in the loop via real-time notifications, dashboards, vendor portals, emails, and interactive Audit Trail Cards, ensuring transparency and ease of engagement.
Do Hyperbots Co-pilots learn from humans?
Yes, Co-pilots incorporate human feedback into their learning cycle through reinforcement learning techniques, improving accuracy and adapting to company-specific practices over time.
Can you give a few examples of human in the loop for Hyperbots Co-pilots?
Invoice Processing: When an invoice has missing or mismatched fields, humans are engaged to verify and make necessary corrections.
Payment Approvals: Payments exceeding predefined thresholds are escalated to senior finance executives for review and approval.
Vendor Onboarding: If a vendor's W-9 form fails automated validation, humans are notified to manually verify and resolve discrepancies.
Tax Validation: For complex tax scenarios where automated rules need further clarification, humans intervene to validate the tax calculation.
GL Coding Adjustments: If the AI’s GL recommendations need modifications, humans provide corrections, which are then learned for future accuracy.
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.