AI search is experiencing a fundamental shift. While most enterprise search solutions operate on indexed, stored data that becomes outdated instantly, a new approach is emerging: AI systems with direct access to live data sources. This isn't just another technical upgrade—it represents a fundamental transformation in how AI operates, learns, and interacts with our data in real-time.
Challenges when AI can't access real-time data sources
During a recent webinar, Qatalog's CEO and founder, Tariq Rauf, highlighted a stark reality from McKinsey research: "Data users are wasting up to 40% of their time trying to surface instant insights." This aligns with industry findings showing that real-time analytics is now the leading use case (71%) for adopting streaming data systems, with nearly three in four organizations citing AI/ML development as their primary driver for streaming data adoption.
As Shaun Clowes, Chief Product Officer at Confluent, who emphasizes the critical nature of data freshness:
"At the end of the day, information has a decay rate. So think about customer feedback. It has a decay rate, or what your competitors are doing has a decay rate. So any new piece of data decays in its value to your decision-making very, very quickly."
This decay rate varies by industry and data type, making real-time access increasingly crucial. Traditional AI implementations face several systemic challenges:
- AI operating on stale data snapshots
- Multiple copies increasing security risks
- Complex permission management across copies
- Potential for "hallucinations" when working with outdated information
- Reduced ability to make real-time decisions in critical situations
Traditional AI implementations were built for a different era. They rely on copying, indexing, and storing data, creating multiple versions of truth and introducing security vulnerabilities.
How AI systems connect to real-time data
The solution isn't about building better indexes—it's about eliminating them entirely.
No-index Retrieval-Augmented Generation (RAG) allows AI to query external knowledge bases for the most current information before generating a response, significantly reducing hallucinations and improving accuracy.
Qatalog's implementation operates on key principles:
1. Zero-copy architecture
- Direct AI connection to source systems
- No intermediate data storage
- Always current information access
2. Sovereign AI security
- Native permissions integration, ensuring data stays within company control
- No persistent data storage, addressing growing concerns about data sovereignty
- Reduced attack surface through the elimination of data copies
3. Intelligent data interaction
- Direct intent-to-data mapping
- Context-aware data access
- Real-time data synthesis
Benefits of giving AI access to live data
In the webinar, Tariq demonstrated how Qatalog's RAG technology acts as an intelligent intermediary between users and their existing systems. Unlike traditional search solutions, Qatalog's process, as Tariq outlines, involves:
1. Intent understanding
When a user asks a question, Qatalog first works to fully understand what they're trying to achieve. As Tariq explains, "It understands the intent... all the various ways of looking at that question."
2. Dynamic data querying
Instead of searching through stored indexes, the system "will query all of the data that's present in all of your system." This means direct, live access to your actual business systems.
3. Smart clarification
The system doesn't just make assumptions. Using the example of a customer value query, Tariq demonstrates how it works: "Most valuable could mean many things. So we go into the data and see what various types of value judgments we can make based off of the data that you've got present in your systems and come back with a clarifying question - Do you mean most valuable by total billing amount, by company size, by lifetime value?"
4. Real-time processing
Once the intent is clear, the system processes the information in real-time. Importantly, as Tariq notes, "The duration of a query is the only time the data is present in our systems. Data flows through our system, answers the query and it goes out."
5. Zero-trace completion
After delivering the answer, the system "ultimately discards all the information that was used to produce that answer." This "no trace policy" ensures that no sensitive information is stored or retained.
How to connect AI to real-time data?
Qatalog's integration process is straightforward: "You just select the integration you like, connected, authorized access, and you're done. When you authorize access, we don't actually copy anything over—it's just a live connection."
This approach aligns with the growing trend toward sovereign AI, where organizations maintain complete control over their proprietary data while still leveraging advanced AI capabilities.
How secure is AI access to real-time data?
Security is built into the direct access model through multiple layers of protection. "We have federated granular ACL user permissions," Tariq explains, "meaning all existing system permissions are automatically respected." This ensures AI systems only access data according to established security protocols.
The system follows a strict no-storage policy. "The duration of a query is the only time the data is present in our systems," Tariq emphasizes. All data transfers are encrypted end-to-end, and no information is stored or retained.
This approach offers unique advantages in an era of increasing data security concerns, as AI systems only interact with data during active queries and then completely release it.
For additional security, Qatalog:
- Inherits existing security permissions
- Respects all access controls
- Maintains end-to-end encryption
- Follows compliance requirements
- Leaves no data footprint after queries
Real-world applications of AI with live data access
The applications of real-time AI are particularly powerful for organizations with complex data needs. For finance organizations with decades of emails and documents, real-time AI provides instant access to archives. Media companies can instantly surface research materials, scripts, and contracts. Sales teams can immediately access customer information across support tickets, product documentation, and communication history.
Related: Discover the best AI assistants for work
Is real-time AI worth it?
The impact of switching to real-time AI is significant and measurable. "Customers and users can safely expect to save at least 2 to 4 hours a week on information retrieval and processing needs," Tariq notes. "Besides saving time, there's also an acceleration component where teams can operate much faster than they did before."
Beyond time savings, organizations see:
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Enhanced decision-making speed
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Improved data governance
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Reduced infrastructure costs
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Better compliance management
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Higher data accuracy
Want to learn more?
Watch the full webinar recording to see how Qatalog's real-time AI technology transforms enterprise search, or schedule a call to discuss your use case.
FAQ about Qatalog
How does AI access real-time data sources?
AI access to real-time data involves direct connections to live data sources like CRMs, analytics tools, or databases without using indexed snapshots. AI systems connect directly to your existing tools and systems through secure API calls, instead of storing or indexing data. This direct system access ensures Qatalog works with fresh data, processes it for your query, and immediately discards it after use.
How is real-time data access different from indexed search?
The key difference is that indexed search relies on pre-stored data snapshots, which can become outdated. In contrast, AI with real-time access, pulls fresh data from live systems, ensuring accuracy and relevance.
Instead of searching stored or indexed data, Qatalog queries all the data in your systems directly. As Tariq explains, "When you authorize access, we don't actually copy anything over it. It's just a live connection into that integration."
What types of data can Qatalog AI connect to in real-time?
Qatalog can connect to and process range of live data sources, including customer insights, sales forecasts, campaign performance metrics, inventory updates, and employee engagement stats. You can even use Qatalog's AI to access the internet in real-time, i.e., to analyze Reddit threads or competitor websites.
Here’s the not-exhausting list of live sources Qatalog can process:
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Salesforce data
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SharePoint documents
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Email (Outlook, Gmail)
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Product documentation
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Customer support tickets
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Contracts and legal documents
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Data warehouses (Bigquerry, Snowflake)
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Database records
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CRM data
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Project management tools
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Business intelligence platforms
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HR systems and records
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Financial data systems
Can Qatalog access multiple live data sources simultaneously?
Qatalog can coordinate multiple API calls and synthesize information from various sources into a single, coherent response while maintaining proper permissions.
Can Qatalog answer specific, complex questions about our data?
Yes, Qatalog can handle nuanced queries and provide clarification. For example, when asked about "most valuable customer," it can clarify "Do you mean most valuable by total billing amount, by company size, by lifetime value?" It can also handle complex queries that require synthesizing information from multiple sources and understanding context.
How does Qatalog scale as our data and tools grow?
Qatalog’s unique artificial intelligence adapts to new data sources and can handle increasing data volumes without sacrificing accuracy.
What technical requirements are needed to integrate Qatalog?
Integrating Qatalog with your live data sources is easy. You can sign up for free, connect the tools you’d like, authorize access, and you're done. It doesn’t require any special infrastructure to get started, but we’re happy to discuss you use case during a call.
How does AI with real-time data access improve reporting and analytics?
Real-time AI provides up-to-the-minute insights that can transform how organizations access and use data. For instance, in a sales organization, consider how often you’ve had to reach out to customer support, dig through internal documents, or search product and enablement resources to find specific information for a client. With real-time AI, you can instantly synthesize the latest customer interactions, active support tickets, and recent product updates to deliver accurate responses faster.
A dedicated AI solution designed for real-time data processing enables dynamic reporting by pulling live metrics that update automatically as new data becomes available. This ensures the insights always reflect the latest changes, adapting responses to shifting parameters.
Real-time AI isn’t limited to sales—it’s valuable in any industry that relies on identifying trends. For example, if your organization has decades of emails and documents stored in archives, real-time Retrieval-Augmented Generation (RAG) can uncover emerging patterns, track changes as they happen, and compare historical data with current trends.
By leveraging real-time AI for reporting, stakeholders can quickly identify issues and respond immediately to changes. This empowers proactive decision-making and problem-solving, helping organizations stay ahead in a fast-evolving landscape.