AI-Ready Data Made Easy
Experience lightning-fast, context-rich, and nuanced business intelligence with RAW. Harness the power of Large Language Models (LLMs) to process the latest data from operational systems like CRM, Finance, HR, and more, and propel your business insights to new heights.
Operational Data at the Fingertips of LLMs
Empower your language models to tap into well-governed operational data in real-time using RAW, our groundbreaking solution that transforms your data into an AI-friendly format. Ensuring data governance and metadata quality are essential steps in maintaining high-quality data free of bias.
LLM-Ready Data
Transform your operational data into formats and structures that are instantly recognizable and usable by AI models, enabling real-time interactions that redefine efficiency. This data preparation process not only supports ai initiatives but also strengthens human-machine relationships by improving transparency in decision-making.
Data On-Demand
Ensure that your LLMs access the most current data by directly connecting them to your operational data sources. With RAW, data doesn’t become a bottleneck; instead, it enables data to be easily accessible and reusable across various business domains.
“Automagic” Data Contextualization
Utilize RAW to automatically enrich your data with meaningful metadata, supercharging the AI language model's understanding and output accuracy. The data lineage and data provenance are meticulously tracked to provide data leaders and data scientists with a clear understanding of its journey, ensuring it remains well-documented and identifiable.
No Hallucinations
With the help of detailed metadata, RAW eliminates errors and enhances the LLM's accuracy and reliability in generating data insights. This approach supports ai-assisted forecast models that are integral to ai-readiness and competitive advantage.
Turbocharged Response Accuracy
Maintain a crystal-clear record of data transformations and lineage, providing unparalleled clarity and trust in the AI-generated outputs for analysts. This transparency is key for stakeholders who rely on properly governed data for strategic ai investments.
Access All Data, Securely
With RAW, unlock a fortress of secure access to all your operational data, controlling which datasets are accessible to the LLM, without ever compromising security. This ensures data maturity and aligns with lighthouse principles that guide responsible artificial intelligence practices within your organization's organizational framework.
All-In-One Data Integration
Seamlessly access and merge data from various sources in real-time, whether structured or semi-structured, enhancing data availability and enriching perspectives. By supporting retrieval augmented generation (RAG), RAW boosts the ai ambition of cios looking to prioritize AI development.
Impenetrable Data Protection
Safeguard your data and systems, preventing system overload and unauthorized access, ensuring your data remains safe. As data-driven organizations strive for advancement in AI, data issues must be minimized to mitigate the dark side of unchecked data usage.
The Complete Solution
for “AI-Ready Data”
Simplify your data management with RAW's all-encompassing solution, designed for easy integration and efficient operation. This allows leaders need to curate use cases effectively, providing a competitive advantage in a landscape where one-third of ai initiatives fail due to inadequate data handling.
Rapid Deployment and Flexibility
Quickly integrate with LLMs and start deriving business intelligence faster with our pre-configured data solutions, offering exceptional agility and rapid deployment capabilities. By following these best practices, your organization can fully realize the benefits of AI and ai-readiness.
Lower Total Cost of Ownership
Reduce your operational costs and complexity with our unified solution, eliminating the need for multiple tools and extensive training. This approach not only simplifies classification processes but also ensures properly governed data is ready for AI applications across all organizational levels.
3 simple steps:
Build, Host, Share!
Build
Build data services to aggregate data from multiple sources in real-time using AI and low-code with built-in IDE.
Host
Host your data services in a managed platform with built-in CI/CD support, testing and API management.
Share
Share your data securely as APIs. Includes built-in data catalog and fine-grained access control.
What is AI Data Readiness: Defining AI Readiness
In the era of digital transformation, the concept of AI readiness has become increasingly crucial as businesses look to harness the power of generative AI and machine learning (ML) to drive innovation. But what exactly does it mean to make data prepped for AI?
AI-ready data refers to data that has undergone meticulous preparation to ensure it can be effectively used in AI language models and ML algorithms. This process is essential for creating trustworthy and accurate training data that will power AI solutions.
Key Aspects of AI-Ready Data
Data Quality: To define AI-optimized data, it's important to ensure that the data is free of errors and inconsistencies. This involves thorough data cleaning processes that eliminate noise and handle missing values, preparing it so it can be successfully deployed in AI applications.
Data Structure: AI-ready data should be organized in a consistent format that aligns with the deep learning model’s needs. Whether dealing with structured or unstructured data, it must be formatted and labeled appropriately to facilitate ML processes. Gartner emphasizes that a well-structured dataset is foundational for successful AI adoption.
Relevance and Context: AI models perform best when fed data relevant to the specific domain or problem they are addressing. Adding contextual metadata can significantly enhance the model's ability to generate accurate and meaningful insights, which is crucial for effective AI automation.
Security and Compliance: Ensuring that data is securely managed and compliant with legal standards is crucial for implementing AI solutions that are not only effective but also trustworthy. Properly managed data also allows organizations to personalize their AI applications, ensuring they meet specific needs while adhering to privacy regulations.
FAQs about AI-Ready Data
Why is AI-ready data important?
AI-ready data is crucial because the performance of AI models depends heavily on the quality and relevance of the training data. Without properly prepared data, these models may produce inaccurate or biased results, undermining their effectiveness.
Can AI work with any data?
AI can work with various data types, including structured, semi-structured, and unstructured data. However, the data must be properly prepared for AI, ensuring the model can effectively process and analyze it within the specific domain of the application.
How do I prepare data for AI?
To make data AI-ready, you need to focus on readiness by cleaning it to remove errors, structuring it to align with the AI model’s needs, enriching it with metadata for context, and ensuring it is securely managed. Tools that facilitate this process can simplify deployment and improve outcomes.
What are some common challenges in preparing AI-ready data?
Common challenges include dealing with large volumes of data, ensuring data quality, handling unstructured data, and maintaining data security and compliance. Overcoming these challenges often requires evaluating specialized tools and implementing best practices.
Explore how people use RAW to automate API building
Built by researchers, committed to open source
RAW is powered by Snapi, our home-grown data analysis language built from the ground up for real-time data analysis. Snapi has a rich built-in library with ready-to-use connectors to the most common data formats, data lakes, and databases.