ContextClue: AI Text Analysis Tool by Addepto

Powered by Generative AI, ContextClue provides automated text analysis tailor-made to your needs.

Business benefits

Advance your document analysis with the power of AI

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About ContextClue
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ContextClue is an AI text analysis tool

It is capable of handling vast amounts of textual data and allowing users to ask specific questions to extract desired information accurately.

What makes ContextClue a unique solution?

ContextClue not only condenses large text files into a concise summary while preserving the key points and overall context.

With the question-driven data extraction feature, users can specify their information needs by asking questions, which the software will answer based on the text input.

How does question-driven feature work?

In MVP, available right after the source code deployment locally, a feature allows the user to ask a question about the document.

The system responds without information about the whole conversation, so the session can be stateless and simpler API can be used.

However, there is no obstacle in adapting more complex APIs (e.g., WebSockets) to make user can conduct a chat-like conversation with the system.)

Key Features

Open Source

ContextClue can be customized and modified to suit specific needs and requirements.

API-first architecture

Users have the freedom to tailor the software to their preferences, extend its functionality, and integrate it with other systems.

Support for variety of Large Language Models (LLMs)

None LLM is hard-coded in ContextClue. Users can choose the ones they can work with and connect them via LangsChain Agents.

Data Privacy

The solution with a simple UI is available after deployment on a server or on a local machine.

ContextClue workflow

ContextClue workflow starts with transforming PDF content into a unified JSON file ready to further analysis. Once the JSON file is uploaded to the database, it is divided into blocks of text and then summarized by chosen LLMs. When summarization is done, LLMs "know" where to find the exact information the user is searching for via LLM prompt and can extract them from the text.

ContextClue workflow

ContextClue processes any PDF

ContextClue workflow

ContextClue workflow – visualization

ContextClue – processes any PDF document chapter by chapter not only to deliver a general text summarization but to extract precise information the user is looking for. ContextClue workflow

Key benefits


Enhancing Time Efficiency & Productivity

ContextClue empowers researchers, students, journalists, businesses, data analysts, and individuals tasked with navigating extensive text volumes to swiftly extract key insights without having to peruse the entire content.

Improved Understanding

Summarization simplifies intricate, technical, legal, or financial documents into more comprehensible and approachable language, enabling individuals without expertise to grasp the core concepts.

Support for Multiple Languages

Text summarization driven by AI can be utilized for content in diverse languages, simplifying the process for users to obtain summaries of documents or articles in foreign languages from across the globe.

Personalization & customization

Generative AI is able to generate summaries tailored to specific requirements or needs. This allows users to receive summaries that focus on specific topics, industries, or areas of interest.


What is Document Analysing Software?
What is the role of AI on document analysis?
Is streamlined document handling the same as AI-powered document analysis?
What is contextual document understanding?
What is data extraction?
What are the typical stages of data extraction used in document analysis tools?
What are the main challenges of automated document processing?
Why API is crucial in tackling integration-related and scalability-related challenges?

What is Document Analysing Software?

Document analysis software it is a type of software application designed to process, interpret, and extract information from various types of documents.

It utilizes techniques from natural language processing (NLP), Optical Character Recognition (OCR), and Machine Learning (ML) to understand and analyze content within documents.

The software can handle a wide range of document formats, including text documents, PDFs, images, spreadsheets, and more.

What is the role of AI on document analysis?

The combination of AI techniques, including OCR, NLP, ML, and computer vision, is the engine that empowers document analysis software.

OCR technology enables text extraction from documents such as invoices, forms, or handwritten notes, making them searchable and editable.

NLP allows systems to understand and analyze the textual content of documents by identifying key topics or themes and extracting structured information from unstructured text.

ML models can be trained to classify documents, detect specific types of content, and computer vision algorithms can extract data from images or scanned documents. AI is the foundation of effective document analysis.

Is streamlined document handling the same as AI-powered document analysis?

Streamlined document handling and AI-powered document analysis are closely related, but they are not the same. Streamlined document handling focuses on optimizing the overall process of managing and handling documents within an organization.

AI-powered document analysis refers specifically to using artificial intelligence (AI) techniques to analyze and extract meaningful information from documents.

What is contextual document understanding?

Contextual document understanding refers to the broad concept of comprehending and interpreting the meaning and context of a document. It involves analyzing the content, structure, relationships, and semantics within a document.

AI-powered document analysis is a subset of contextual document understanding, with the former focusing on specific AI techniques applied to document analysis tasks, and the latter encompassing a more comprehensive understanding of the document’s context and meaning.

What is data extraction?

Data extraction involves retrieving and capturing relevant information from various sources, such as documents, databases, websites, or other structured or unstructured data formats. It aims to transform them into a structured format that can be easily analyzed.

What are the typical stages of data extraction used in document analysis tools?

  • Data Source Identification that includes physical documents, electronic files, databases, websites, or APIs.
  • Data Parsing to extract relevant information and converting it into a format suitable for further processing. This may involve techniques such as text parsing, OCR (if dealing with scanned documents), or data cleansing.
  • Data Transformation and Integration that involves mapping the extracted data fields to a predefined schema or performing data normalization to ensure consistency.

What are the main challenges of automated document processing?

Dealing with unstructured data
Documents often contain unstructured or semi-structured data, which can include free-form text, tables, images, or a combination of different data types.

Data Variability
Documents vary significantly in terms of layout, formatting, language, vocabulary, or domain-specific terminology. AI models harnessed in advanced document analysis tools need to be trained on diverse and representative data to ensure generalization on the expected level.

Data Privacy and Security
Compliance with data protection regulations or internal security policies is a challenge that requires appropriate data anonymization, encryption, access controls, and secure infrastructure.

Scalability and Efficiency
AI models need to be scalable to handle the increasing amount of documents, while also being computationally efficient to provide real-time or near-real-time analysis.

Integration with Current Infrastructure
Integrating AI-driven document analysis solutions with existing document
management systems, workflows, or enterprise software can be complex.

Why API is crucial in tackling integration-related and scalability-related challenges?

APIs provide a standardized, simplified, and flexible way to integrate AI-driven document analysis capabilities into existing systems. They enable seamless communication, real-time integration, scalability, and customization.

APIs simplify the development effort and ensure interoperability, facilitating the adoption and utilization of AI-driven document analysis solutions within organizations’ existing workflows and systems.

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