Glossary
Key terms and definitions in data integration and automation
A
Adaptive Learning
A sophisticated AI capability that continuously improves and refines its performance based on new data and feedback, enabling systems to become more accurate and efficient over time.
Adaptive Parsing
An intelligent document processing approach that dynamically adapts to different document formats and structures, learning from each interaction to improve extraction accuracy.
Adaptive Routing
A smart document distribution system that automatically determines the optimal path for documents based on content, context, and business rules.
AI (Artificial Intelligence)
The simulation of human intelligence by machines, enabling them to learn, reason, and make decisions. It powers applications like natural language processing, image recognition, and data analysis.
C
Chunking
The process of breaking down large documents or datasets into smaller, manageable pieces for efficient processing and analysis.
Colpali
An emerging AI methodology that combines collaborative pattern learning and intelligent adaptation for enhanced data processing capabilities.
Custom AI
Specialized artificial intelligence models tailored to specific business needs and use cases, offering enhanced performance for domain-specific tasks.
E
Embeddings
Dense vector representations of data, such as text or images, that capture meaning in a machine-readable format. Used for tasks like similarity search and clustering.
ETL (Extract, Transform, Load)
A data integration process that collects data from multiple sources, transforms it into a usable format, and loads it into a target database or system.
F
Fine-Tuning
A technique where a pre-trained AI model is further trained on specialized data to adapt it for specific tasks or domains.
Foundational Models
Large-scale AI models trained on diverse and extensive datasets, serving as a base for specialized applications like natural language understanding and vision processing.
G
Generative AI
AI systems capable of creating new content, such as text, images, or music, by leveraging large pre-trained models like GPT.
H
Hybrid Search
A search approach combining traditional keyword-based search with semantic search, ensuring comprehensive results by balancing precision and context-awareness.
HyDE (Hypothetical Document Embeddings)
A method that uses AI to generate hypothetical document representations for improved retrieval and clustering by matching abstract queries to real-world data.
I
IDP (Intelligent Document Processing)
Advanced technology that combines AI and machine learning to automatically extract, classify, and validate information from various document types.
IPA (Intelligent Process Automation)
A combination of artificial intelligence and robotic process automation that enables smart, adaptive automation of complex business processes.
K
Knowledge Base
A structured repository of information designed to be easily accessible for both humans and AI, enabling efficient decision-making and problem-solving.
L
LightRAG
A streamlined version of Retrieval-Augmented Generation (RAG), optimized for lightweight or resource-constrained applications while maintaining accuracy and performance.
LLM (Large Language Model)
Advanced AI models trained on vast datasets to perform tasks like generating human-like text, answering questions, and extracting information.
N
NLP (Natural Language Processing)
A subfield of AI focused on the interaction between computers and human languages, enabling tasks like translation, sentiment analysis, and information retrieval.
O
OCR (Optical Character Recognition)
A technology that converts images of text into machine-readable data, enabling tasks like digitizing documents and automating data entry.
R
RAG (Retrieval-Augmented Generation)
A technique that integrates external knowledge retrieval into AI text generation, improving accuracy and relevance in outputs.
RDB (Relational Database)
A database structured to store data in rows and columns, facilitating efficient querying and management of relational data.
RDP (Robotic Document Processing)
Automated system that uses robotic process automation to handle document-based tasks, including data extraction and validation.
S
Semantic Search
An AI-powered search technique that focuses on the meaning of words and context rather than exact keyword matches, delivering highly relevant results.
Structured Data
Information organized in a predefined format, such as databases or spreadsheets, making it easily searchable and analyzable.
T
Tokenization
The process of breaking text into smaller units, such as words or phrases, to make it machine-readable for AI and language models.
Transformers
A type of neural network architecture used in AI, particularly for natural language processing tasks, that excels in understanding context and relationships in data.
Transfer Learning
A machine learning approach where a model trained on one task is adapted for another, reducing the data and time required for training.
U
Unstructured Data
Information that doesn't follow a predefined data model, such as text documents, emails, and images, requiring specialized processing for analysis.
V
Vector Database
A specialized database optimized for storing and querying vector embeddings, critical for applications like semantic search and AI-driven recommendations.
Vectors
Numerical representations of data, such as text or images, used in machine learning to analyze and process complex information efficiently.
Vision
A field of AI focused on interpreting and understanding visual information from images or videos, enabling tasks like object detection and image classification.
VLMs (Vision Language Models)
AI models that can understand and process both visual and textual information, enabling tasks like image captioning and visual question answering.
Z
Zero-Shot Learning
A capability in AI where models perform tasks they weren't specifically trained for by leveraging knowledge from related domains or tasks.