GiQWorkflows:
LLM-enabled
data processing
Use GiQ Workflows to process, enrich, and interpret large unstructured datasets effortlessly with Generative AI.
GenAI-powered data
processing
Use LLM-driven workflows to automate data preparation, including normalization,
categorization, and summarization, improving efficiency and accuracy.
LLM data enrichment
Use public or private LLM models to extract key information, add context, and tag semantic relationships within the data for a more comprehensive view.
Enriched datasets preparation for further analysis
Make your unstructured data easily accessible and prepared for in-depth analysis, ready to be transformed into actionable insights.
Enhance data
processing efficiency: key GiQ Workflows features
Explore tools and features designed to streamline and improve your data processing and enrichment operations.
Unlimited workflows
Create and run an unlimited number of workflows.
Pre-configured LLM steps
Ready-to-use steps for common LLM-based data operations.
Built-in prompt library
Use pre-defined prompts for data extraction, classification, or summarization.
Custom prompts
Create reusable custom prompts to fit your specific needs.
Data connectors
Read data from and write results to a variety of sources.
Connectors to public LLM models
Use OpenAI GPT, Anthropic Claude, or Google Gemini models via public APIs.
Connectors to private LLM models
Use custom models running in Amazon Bedrock or Google Vertex AI.
Flexible deployment options
Deploy in any cloud (AWS, Azure, Google) or on-prem infrastructure.
Optimizing data for insight extraction:
crucial GiQ Workflows benefits
Data analytics professionals rely on GiQ Workflows to prepare their enterprise data for powerful analytics.
FAQ: what you need to know about data enrichment and processing
Data enrichment and data processing are essential for businesses to gain valuable insights and make informed decisions. Learn more about it here.
What is data processing?
Data processing is the systematic approach of transforming raw data into meaningful information through a series of steps, including data collection, preparation, input, processing, output, and storage. This entails sourcing data from various origins, cleaning and structuring it to correct discrepancies, and employing computational and statistical methods to transform it. The refined data is then displayed in user-friendly formats such as reports or charts and preserved for future analysis.
Why is data processing important?
Data processing is crucial for transforming raw data into meaningful and actionable information. By cleaning and organizing data, processing ensures accuracy and reliability, reducing the risk of errors. This capability helps organizations to distill vast amounts of data into key insights, driving better decisions and innovation.
What is data enrichment?
Data enrichment is the process of enhancing existing data by adding additional information from external or internal sources. This supplementary data provides more context, detail, and value, making the original data more comprehensive and useful. Data enrichment can involve various methods, such as appending demographic information to customer records, incorporating geographic data into sales data, or integrating social media insights into marketing data. The goal of data enrichment is to improve the quality and utility of data.
How to use LLMs for data enrichment?
Large Language Models can be used for data enrichment by enhancing existing datasets with additional context and structured information. They can generate detailed descriptions, summaries, and explanations, adding valuable context to raw data. LLMs can also perform entity extraction, identifying and extracting key entities such as names, locations, and organizations from unstructured text, thereby structuring the data. Additionally, LLMs can analyze sentiment in textual data, providing insights into customer opinions and feedback. These capabilities improve the depth, accuracy, and utility of the data.
Still have questions?
Contact the GiQ team.