Self Learning Capabilities
Hyperbots Co-pilots leverage self-learning to adapt to company-specific workflows, refine accuracy through inference-time learning, and improve over time based on human interactions.
Key Features
Automated Adaptation to Company-Specific Processes
Co-pilots continuously learn from human decisions and adapt to the unique workflows, preferences, and policies of each company.
Enhanced GL Code Recommendations
The GL Recommendation Agent leverages self-learning to refine GL code assignments based on company-specific patterns and human overrides.
Continuous Improvement in Accuracy
Wherever needed, pre-trained models are refined through inference-time learning to enhance performance and meet specific organizational requirements.
Incremental Confidence Building
The Co-pilots apply self-learned changes only after building confidence through repeated human actions, ensuring trust and reliability.
Dynamic Adaptability Across Processes
The self-learning capability is applicable across various Co-pilot functions, making them dynamically adaptable to changing company policies and practices.
Comparison with competition
FAQs: Self Learning Capabilities
Can you give examples of self-learning capabilities of Hyperbots Co-pilots?
Hyperbots Co-pilots use self-learning to enhance performance over time. For example:
GL Recommender Agent learns from historical GL coding and human corrections to suggest accurate GL codes for future invoices.
Payment Timing Recommendations improve based on past payment patterns, vendor responses, and discount optimizations.
Invoice Matching adjusts tolerance levels based on repeated human overrides to improve future matching accuracy.
Why is self-learning important?
Self-learning ensures that Co-pilots adapt to company-specific practices and evolving business needs. It reduces manual interventions over time, improves efficiency, and maintains high accuracy, enabling automation to remain relevant in dynamic environments.
If your Co-pilots are pre-trained, why do they require self-learning?
For most tasks, Co-pilots are pre-trained to achieve near 100% accuracy, but some tasks, like GL recommendation, require company-specific training. Just like humans continuously learn new contexts to adapt, Co-pilots need self-learning to refine their understanding and handle unique organizational practices effectively.
How are Hyperbots' self-learning capabilities different from those of competitors?
Hyperbots’ self-learning is designed for finance and accounting tasks, leveraging specialized models for industry-specific nuances. Unlike generic solutions, Hyperbots focuses on structured, actionable learning within strict compliance and accuracy requirements, ensuring it delivers tailored improvements without sacrificing precision.
How do you make sure that your Co-pilots do not learn bad practices?
Hyperbots Co-pilots use controlled reinforcement learning, where self-learning is restricted to validated, high-confidence data points. Human approvals and overrides are monitored, and outliers or anomalies are flagged for review. This ensures that Co-pilots do not perpetuate errors or adopt undesirable practices.
Is self-learning company-specific? How do you ensure that my data is not used for general training?
Yes, self-learning is company-specific. Hyperbots does not use customer data for general model training. All learning occurs in isolated environments, ensuring that company data remains secure and confidential, with no risk of cross-company data leakage.
How do Co-pilots build confidence in the data they learn from?
Co-pilots build confidence through validation and reinforcement mechanisms. For example:
They compare learned patterns against multiple data points (e.g., historical trends, current policies).
They require consistent approval or confirmation from human users over repeated tasks before automating decisions independently.
At what point does self-learning stop?
Self-learning evolves continuously but can be restricted based on company policies. For example, once the Co-pilot achieves consistent accuracy and meets predefined performance thresholds, further learning can be paused or monitored to maintain stability. Companies can also configure learning limits for specific processes.
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.
