HelpingAI offers a large language model that uses a Chain of Recursive Thoughts architecture to perform up to 50 iterative reasoning cycles per query, delivering higher accuracy on complex tasks while using ten times fewer tokens than typical LLMs. The platform includes Parallel Empathetic Threads and Structured Emotional Reasoning for nuanced, context‑aware responses, and is available through a transparent pay‑as‑you‑go token pricing model for developers and enterprises.
Funding
Funding not disclosed
Founders
Product
Problem
Current large language models often struggle with complex, multi-step problems, producing inaccurate or incomplete answers and requiring extensive computational resources. This limits their usefulness for applications that need reliable, deep reasoning while keeping costs manageable.
Solution
HelpingAI delivers a large language model built around a Chain of Recursive Thoughts (CoRT) architecture that can perform up to 50 reasoning cycles within a single response. By iteratively analyzing, refining, and self‑correcting its logic, the model achieves higher accuracy on difficult tasks without excessive token consumption. The platform also incorporates Parallel Empathetic Threads (PET) and Structured Emotional Reasoning (SER) to handle nuanced, context‑aware queries. Users access the service through a transparent, token‑based pricing model, allowing them to scale usage according to workload while benefiting from the model’s efficiency‑first design.
Target Audience
Primary customers are developers, enterprises, and AI‑driven product teams that require high‑accuracy, cost‑effective reasoning for complex problem solving, data analysis, or conversational applications.
Features
- Chain of Recursive Thoughts enables up to 50 iterative reasoning cycles per query
- Parallel Empathetic Threads (PET) for simultaneous context handling and empathetic response generation
- Structured Emotional Reasoning (SER) to incorporate emotional nuance into outputs
- 10x token efficiency compared to standard LLMs, reducing compute cost
- Multiple model sizes (HelpingAI‑3.1 16B, Dhanishtha‑2.0 15B, Dhanishtha‑2.0‑mini 5B) to match performance and budget needs
- Adaptive Effort Intelligence adjusts reasoning depth dynamically across four effort levels
- Pay‑as‑you‑go pricing based on input and output tokens, with transparent rates per MTok