Unstract harnesses the power of AI to automate critical business processes involving unstructured documents, driving digital transformation across industries. Its innovative open-source platform leverages Large Language Models (LLMs) to deliver scalable, no-code solutions for document automation. With features like LLMWhisperer and LLMChallenge, Unstract ensures exceptional accuracy and reliability while extracting data directly from complex documents, regardless of format or layout, without requiring training. By transforming unstructured information into structured, actionable data, the platform empowers businesses to streamline operations and make informed decisions efficiently. Serving industries such as finance and insurance, Unstract redefines Intelligent Document Processing (IDP) and Robotic Process Automation (RPA) capabilities. It supports seamless integration with business ecosystems, enhancing data flow and productivity. As an open-source solution, it invites organizations to revolutionize their document workflows with cutting-edge technology. Backed by Lightspeed and Together Fund, Unstract represents the future of unstructured data processing.
Hourly Rate(INR):
NA
Budget(INR):
NA
Hourly Rate(USD):
< $25
Budget(USD):
$1000+
Employees:
50-249
Headquarters:
Los Altos, United States
Established:
2023
Services Provided
LLMWhisperer optimizes complex documents for LLMs, enhancing input quality for precise output. It preserves layouts, handles multi-column formats, detects handwritten text, checkboxes, and radio buttons, and processes scanned PDFs or camera-captured documents seamlessly.
LLM APIs often charge per token processed, making token calculation essential for managing and predicting costs, particularly in large-scale or commercial use cases.
LLMs have token-based context limits, often 2048 or 4096 tokens. Tracking token counts ensures inputs stay within these limits, preventing issues with processing and maintaining model effectiveness.
Effective prompt crafting requires managing token counts to include essential details while staying within model limits. This is crucial for complex tasks that demand clear, detailed instructions for optimal results.
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