AI Native
Hyperbots Co-pilots follow an AI-native approach, prioritizing purpose-built AI models for each use case, ensuring precision and transformative productivity gains of up to 90%.
Key Features
AI-first design philosophy
Hyperbots Co-pilots are designed with an AI-first approach, where AI models are developed specifically for each use case before building functionality around them.
Purpose-built AI for key tasks
AI elements are meticulously crafted to address specific business needs within finance workflows, ensuring precision and efficiency.
Transformative productivity gains
Unlike superficial AI layers or patchwork on traditional rules-based systems, Hyperbots Co-pilots achieve transformational productivity improvements of up to 90%, compared to 20-30% from competitors.
Deep agentic AI for finance tasks
Hyperbots leverages deep agentic AI to tackle complex tasks across finance processes, enabling Co-pilots to deliver results with minimal human touch.
Sustainable value through innovation
The AI-native approach positions Hyperbots to deliver long-term value, enabling continuous innovation and deeper automation for evolving customer needs.
Comparison with competition
FAQs: AI Native
What is the difference between AI-native and non-native software for the CFO's office?
Why can traditional applications patched with AI capabilities not deliver the same outcomes as AI-native Co-pilots of Hyperbots?
What is the difference in productivity gains between AI-native and non-native CFO applications?
Which tasks cannot be automated deeply enough by non-native AI applications?
What is the approach difference in innovation and design thinking for AI-native vs. post-facto AI applications?
Why can deep Agentic AI not be developed with non-native AI thinking?
How can you demonstrate the difference between Hyperbots’ AI-native approach and traditional software for the CFO office?
Why Hyperbots Agentic AI Platform?
Finance specific
Best-in-class accuracy
Synthesis of unstructured and strutured finance data
Pre-trained agents with state of the art models
Company specific inference time learning
