In today's data-driven world, organizations increasingly rely on AI-powered search and knowledge retrieval tools to sift through vast amounts of information. Traditionally, these tools depend on data indexing, which involves copying and storing data in vector databases for quick retrieval. While this method has been standard practice, it presents significant challenges, especially for enterprises dealing with sensitive information.

This blog post explores the drawbacks of data indexing in AI search and introduces Medullar's alternative solution.

Medullar offers AI-powered answers without indexing data or just indexing the necessary data, no more, no less.

Most AI search engines index data to enable fast retrieval. This process converts data into numerical representations (vectors) and stores them in a vector database. While effective for speed, this approach has several downsides:

1. Reduced Data Security: Copying and storing data—even in vector form—on third-party servers increases the risk of data breaches and exposes sensitive information to potential attacks.
2. Lower Precision with Large Datasets: As the volume of data grows, the precision of search results can diminish due to the "curse of dimensionality" in high-dimensional vector spaces.
3. Complex Data Management: Maintaining and updating indexes requires significant resources and coordination, leading to increased operational overhead.

Let's delve deeper into each of these challenges.

1. Reduced Data Security

Data Vulnerability

When data is indexed, copies are stored on external servers, which expands the attack surface for potential hackers. In a security breach, attackers could access not just a single application's data but a consolidated repository containing sensitive information from multiple sources.

Real-World Example

Consider a financial institution that indexes customer data for AI search purposes. Confidential information like account numbers, transaction histories, and personal details could be exposed if the indexing server is compromised, leading to severe legal and financial repercussions.

Embeddings Are Not Immune

It's a common misconception that vector embeddings are secure because they are numerical and seemingly indecipherable. However, a study by researchers at Cornell University demonstrated that up to 92% of the original data meaning can be reconstructed from embeddings. This finding emphasizes that embeddings should be treated with the same security considerations as raw data.

2. Lower Precision with Large Datasets

The Curse of Dimensionality

As datasets grow, the high-dimensional space where vectors are stored becomes increasingly sparse. This sparsity makes it difficult to accurately identify the nearest neighbor vectors during searches.

Impact on Search Results

With more data points, the distance between vectors becomes less significant, making distinguishing relevant results from irrelevant ones challenging. This can result in:

• False Positives: Irrelevant data points are identified as relevant, leading to inaccurate search results.
• Reduced Accuracy: The AI may generate responses based on incorrect or less relevant information, undermining user trust.

Illustrative Example

Imagine an AI search tool indexing millions of medical records to assist in patient diagnosis. Due to decreased precision, the tool might retrieve and present information from unrelated cases as the data grows, potentially leading to misdiagnoses.

3. Complex Data Management

Operational Overhead

Managing an index requires continuous effort:

• Data Mapping: Aligning diverse data sources accurately.
• Deduplication: Identifying and merging duplicate records.
• Pipeline Monitoring: Ensuring data flows smoothly without errors.
• Data Cleaning: Maintaining high data quality through regular cleansing.

Resource Allocation

Enterprises often need dedicated teams to handle these tasks, diverting resources from core business functions. The complexity increases with the number of data sources and the rate at which new data is generated.

Scenario Illustration

A multinational corporation using various CRM, ERP, and HR systems faces the challenge of synchronizing the indexed data across all platforms. This synchronization demands substantial coordination and technical expertise, inflating costs and complicating system maintenance.

Medullar introduces an innovative approach combining federated search and retrieval augmented generation (RAG) to deliver AI-powered answers without indexing your data or just indexing the necessary data per user request. Here's how Medullar addresses the challenges:

1. Enhanced Data Security

• No Data Copying or Storage: Medullar fetches information directly from the source in real time and does not store or index the data.
• In-Memory and Controlled Storage Options: Users can choose in-memory spaces where data is forgotten immediately after the session ends or opt for semi-permanent storage with customizable time-to-live (TTL) settings in a secure, encrypted vector database.
• Minimized Attack Surface: Eliminating data duplication reduces potential points of failure, enhancing overall security.

2. High Accuracy and Precision

• Real-Time Data Retrieval: Medullar pulls the latest data directly from the source, ensuring up-to-date responses.
• Reduced Hallucinations: The AI provides more accurate and contextually relevant answers using current data rather than pre-indexed information.
• Scalability: The system maintains high precision even as data volumes grow, making it suitable for enterprises with large datasets.

3. Simplified Data Management

• Elimination of Index Maintenance: Without an index to manage, organizations can avoid the complexities associated with data mapping, deduplication, and pipeline monitoring.
• Ease of Integration: Medullar integrates seamlessly with existing systems via APIs, reducing the need for extensive configuration.
• Resource Optimization: Teams can focus on core activities rather than managing and updating data indexes.

• Security First: Keep your data safe with a solution that doesn't require copying or storing your information.
• Accurate Responses: Benefit from AI answers generated from the most current data.
• Operational Efficiency: Simplify your data management processes and reduce overhead costs.

Experience the advantages of AI-powered search without the drawbacks of data indexing. Medullar offers a 14-day free trial and the option to book a live 1:1 demo with an AI specialist to guide you through the product's features, security measures, and enterprise support capabilities.

While data indexing has been a staple in AI-powered search solutions, its limitations pose significant challenges for enterprises, especially concerning security, precision, and data management complexity. Medullar's approach offers a viable alternative that addresses these issues head-on.

By eliminating the need for data indexing, Medullar enhances security and accuracy and simplifies the overall management of enterprise data. Organizations can now leverage AI-powered search capabilities more effectively, ensuring better outcomes and a stronger focus on their core business objectives.

Explore Medullar today and transform the way your organization handles AI-powered search.