Process Specific Capabilities
Hyperbots Co-pilots, built on the Agentic AI platform, leverage domain-specific training and collaborative agents to deliver process-specific automation.
Key Features
Domain-Specific Training
Co-pilots are trained on datasets and workflows relevant to their specific functions, ensuring high accuracy and adaptability.
Reusable Agents Across Processes
Shared agents are utilized in multiple Co-pilots but adapted to different process contexts and use cases.
Collaborative Co-Pilot Functionality
Co-pilots collaborate seamlessly to execute hybrid tasks by exchanging data and aligning workflows.
Scalable and Integrated Process Execution
The Agentic AI platform enables Co-pilots to scale and adapt dynamically, ensuring unified execution across complex workflows.
Comparison with competition
FAQs: Process Specific
Do Hyperbots Co-pilots process and task specific? Can you give examples?
Yes, Hyperbots Co-pilots are designed to be process and task specific. For example:
The Invoice Processing Co-pilot handles tasks like invoice validation, matching, and GL coding.
The Payments Co-pilot focuses on payment scheduling, approvals, and reconciliations.
The Vendor Management Co-pilot manages onboarding, vendor verification, and updates.
Each Co-pilot is tailored to execute its specific process with precision.
Do you provide process-specific training to agents?
Yes, Hyperbots agents are pre-trained on extensive datasets relevant to their specific process, such as invoices for the Invoice Processing Co-pilot and identity documents for the Vendor Management Co-pilot. Additional fine-tuning is performed during implementation to adapt to company-specific needs.
How do the agents or Co-pilots of various processes collaborate wherever needed?
Agents collaborate seamlessly via the Hyperbots Agentic AI platform. For example:
The Invoice Processing Co-pilot and Accruals Co-pilot share GL coding data to ensure consistent financial entries.
What do you do when the same task exists across various processes? Do you have reusable agents?
Yes, Hyperbots uses reusable agents for common tasks like GL coding, validation, and matching.
The GL Recommender Agent is utilized in both the Invoice Processing and Accruals Co-pilots for consistent coding across processes.
How do you build process-specific nuances in agents?
Process-specific nuances are built using domain-specific training datasets, rule engines, and no-code configurations. For example:
Industry-specific tax rules are incorporated into the Sales Tax Verification Co-pilot.
Unique vendor verification workflows are added to the Vendor Management Co-pilot based on company requirements.
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.
