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Overview

Storytell enables deep data analysis through natural language queries, overcoming the constraints of traditional Large Language Models (LLMs). By leveraging a unique “MapReduce for LLMs” approach and an intelligent LLM Router, Storytell processes massive datasets efficiently, ensuring accurate and actionable insights.

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The Challenge of Context Windows

LLMs have a limited context window, restricting the amount of information they can process at one time. This poses a challenge for analyzing large datasets, as standard models can only consider a fraction of the available data. Context Window Limitations

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Storytell’s Solution: MapReduce for LLMs

Storytell overcomes traditional LLM context window limitations through our innovative “MapReduce for LLMs” approach.
1

Query Analysis

Complex questions are intelligently broken down into smaller, manageable sub-questions
2

Data Segmentation

Information is transformed into Chunks, grouping related concepts together
3

Parallel Processing

Our LLM Router assigns each sub-question to the most appropriate AI model:
  • Reasoning & Knowledge
  • Scientific Analysis
  • Quantitative Processing
  • Code Generation
  • Communication
4

Result Aggregation

Individual responses are combined into a comprehensive, coherent answer
MapReduce for LLMs
While traditional LLMs are limited to context windows of 32k-128k tokens, Storytell’s MapReduce approach enables processing of up to 10 million tokens in a single query.