Drastically increase the signal-to-noise ratio in your data

Unlock AI’s Full Potential with Event-Native Data

Kurrent gives your AI systems instant access to real-time and historical data in high fidelity, simply and efficiently

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LLMs are natural language systems. 
Events are the natural language of business.

It’s hard to get the right data to AI models. It typically requires significant data preparation, and if LLM training data isn’t clean or is flawed, AI systems are more prone to incorrect answers and hallucinations.

Kurrent’s event-native data platform offers a revolutionary solution that aligns perfectly with how modern AI systems process information – particularly for generative AI and agentic AI workflows.
the-natural-synergy-between-events-and-ai

The natural synergy between events and AI

LLMs, at their core, need context in order to produce viable outcomes. Event-native data platforms inherently provide more context because they identify the ‘why’ and ‘when’ something happened, creating a perfect symbiosis with AI technologies. When your data speaks the same language as your AI, magic happens.

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Get context straight “out of the box” with Kurrent

Spend less time shaping data. Kurrent can input events and provide context right away with less transformation or prep needed. LLMs require very little information and raw events come straight out of Kurrent — in some cases without vectorization or transposition required. Kurrent runs in the cloud or on premises, as close as possible to your AI workloads and language models.

why-traditional-data-models-fall-short

Why traditional data models fall short

Traditional state-based data models:
  • Overwrite historical data with each update
  • Lose vital context about how and why changes occurred
  • Limit the granularity of information available to AI systems
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The event-native advantage for AI systems

Events provide fine-grained detail on what happened in your business workflow and, therefore, are ideal as input for AI and ML use cases. Kurrent’s event-native data platform preserves every business event as an immutable record, providing:

  • Rich contextual information about the "why," "how," "who" and "when"
  • Highly granular data that enables more sophisticated AI analysis
  • Complete historical records for training and analysis
  • Real-time data streams for immediate AI processing
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Contextualizing the Kurrent State of Your Business

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Key technical benefits of Kurrent for AI systems

 

Streamlined Data Pipelines

  1. Minimal data transformation required
  2. Direct integration with AI models
  3. Reduced latency in data access

Enhanced Security and Compliance

  1. Immutable audit trails
  2. Granular access control
  3. Complete traceability

Operational Flexibility

  1. Multiple deployment options
  2. Proven multi-language client SDKs
  3. Enterprise-grade security built in

Future-Ready Architecture

  1. Adaptable to emerging AI technologies
  2. Scalable data infrastructure
  3. Support for real-time and batch processing

Kurrent and AI

 
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Improve your AI initiatives

 

Conversational AI enhancement

Build more intelligent chatbots and customer service interfaces that can:
  • Access detailed historical context
  • Provide accurate, real-time status updates
  • Handle complex customer queries with complete information
  • Deliver personalized responses based on full customer journey data

Decision augmentation

Power sophisticated Retrieval-Augmented Generation (RAG) systems that:
  • Assist employees with data-driven decision making
  • Provide real-time insights based on historical patterns
  • Enhance human judgment with AI-powered recommendations
  • Maintain full audit trails of decision processes

Accelerate your AI journey

 
By capturing business events with full context, Kurrent’s event-native platform creates a foundation that allows AI systems to understand your business as it happens. This approach not only enhances current AI capabilities but ensures your data infrastructure is ready for the next generation of AI innovations.
Kurrent eliminates common AI implementation challenges by:
  • Simplifying data retrieval and querying
  • Improving data granularity and context
  • Delivering high-fidelity AI training data
  • Enabling real-time AI applications
  • Strengthening data security
  • Streamlining infrastructure requirements

 

Don't let traditional data architectures hold back your AI initiatives. Embrace the power of event-native data to unlock the full potential of AI in your organization.

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