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KODA Intelligence

Automation powered by generative AI within controlled frameworks perfectly fitting your business needs.
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Graphic overview of KODA Intelligence 3 core elements: rule-based model, Machine Learning Module, Generative AI Module Graphic overview of KODA Intelligence 3 core elements: rule-based model, Machine Learning Module, Generative AI Module

Context is king

We combine generative AI with rule-based models to choose the right response method for every query.

Rule-based model

Precision and full control. Ideal for answering frequently asked questions and automating repetitive processes.

ML Module

A properly trained Machine Learning module, managed directly from the platform. It handles analysis processes and detects anomalies in user behavior. Great also for personalized responses based on previous interactions.

Generative AI Module

A comprehensive solution for testing, evaluating, and monitoring various language models (LLMs), designed to build intelligent AI agents within the platform.

Reliable service quality

You can be sure your AI assistant works correctly after every change – both internal updates and LLM environment upgrades.

Top-level security

With advanced testing tools, you can rest easy knowing nothing reaches users before thorough internal verification.

LLM power under control

You gain a secure framework for effectively leveraging LLMs in the automation of your business processes.

Tamed AI, trusted results

We control LLM behavior through the built-in functions of the Generative AI module, ensuring maximum deployment security and predictable agent performance.

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Knowledge Base

  • A structured collection of unique data – the foundation for AI-driven automation
  • The LLM automatically triggers the appropriate function, passing the required parameters
  • AI delivers instant, verified answers to your customers and employees
  • Integration with any system: documents, websites, product databases, external APIs
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Evaluators

  • Automatic (LLM-as-a-judge) and manual evaluation of response quality
  • Detecting hallucinations, factual errors, and inconsistencies by continuous monitoring
  • Tracking results and highlighting priority improvements, the system keeps learning
  • Minimizing risk by catching inappropriate content before it reaches users
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Queries – full visibility into AI assistant activity

  • Monitoring model queries in real time, with detailed insight into statuses and anomalies
  • Analyzing conversations along with context, prompt history, and model decisions
  • Measuring response time, token usage, and costs in one place – with filtering and comparison options
  • Both automated and manual response quality evaluation within a single dashboard
Available soon

Playground – automated testing of AI model responses

  • Monitoring the quality of responses generated by LLMs
  • Automatically comparing results across different models
  • Verifying whether newly released LLM versions meet quality requirements
  • Assessing the impact of knowledge base updates and prompt modifications on response accuracy
  • Confirming that new integrations work properly