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ease
features
design
support

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Write a Review

Description

Various business operations often lead to the generation of numerous documents, yet many struggle to efficiently track everything and locate similar or related information pertaining to each document, context, or phrase. Luma7 addresses these challenges by automatically creating connections between documents without relying on keywords, metadata, or tags. Our algorithms analyze the text within each document to uncover all potential relationships by comprehending the deeper connections that exist. This approach allows for effective management of all produced documents, facilitates linkages across diverse sources of documentation, and supports fully automated link creation among multiple documents, as well as relationships between concepts and documents. Additionally, we offer automated tagging for each document, which includes identifying relevant keywords not explicitly mentioned in the text, streamlining connection processes across our entire archive of articles, implementing entity recognition throughout documents, providing support in multiple languages, and much more. With Luma7, businesses can improve their document management practices and enhance their ability to find and connect vital information seamlessly.

Description

Lilac is an open-source platform designed to help data and AI professionals enhance their products through better data management. It allows users to gain insights into their data via advanced search and filtering capabilities. Team collaboration is facilitated by a unified dataset, ensuring everyone has access to the same information. By implementing best practices for data curation, such as eliminating duplicates and personally identifiable information (PII), users can streamline their datasets, subsequently reducing training costs and time. The tool also features a diff viewer that allows users to visualize how changes in their pipeline affect data. Clustering is employed to categorize documents automatically by examining their text, grouping similar items together, which uncovers the underlying organization of the dataset. Lilac leverages cutting-edge algorithms and large language models (LLMs) to perform clustering and assign meaningful titles to the dataset contents. Additionally, users can conduct immediate keyword searches by simply entering terms into the search bar, paving the way for more sophisticated searches, such as concept or semantic searches, later on. Ultimately, Lilac empowers users to make data-driven decisions more efficiently and effectively.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cohere
Docker
Hugging Face
OpenAI
Python

Integrations

Cohere
Docker
Hugging Face
OpenAI
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Luma7

Country

Switzerland

Website

luma7.com/biz/biz7.php

Vendor Details

Company Name

Lilac

Country

United States

Website

www.lilacml.com

Product Features

Text Mining

Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering

Product Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Alternatives

Alternatives

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