We tailor our chatbots to meet the specific requirements of each client. Below are examples of chatbots that vary in terms of use cases, level of sophistication, and integration with knowledge bases.
Example use case: A banking chatbot that pulls data from regulations, agreements, and fee schedules to answer customer questions about charges, limits, and loans.
Example use case: An HR assistant answering questions about vacation policies, onboarding, or employee benefits.
Example use case: A legal chatbot finding precedents and legal interpretations from the company’s internal database.
Example use case: A chatbot answering, “What was B2B revenue for Q1 2025?” using data from Power BI.
Example use case: A fashion chatbot suggesting outfits based on catalog data and user preferences.
Example use case: A technical chatbot teaching new employees how to operate a specific ERP system.
Example use case: A chatbot in an IT company informing the project manager about the current progress of a backend component.
Voicebots powered by Large Language Models (LLMs) like GPT, Claude, or Mistral are growing in popularity across industries due to their ability to conduct natural, contextual, and dynamic voice conversations.
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Mailbots — automated systems for managing email communication — are becoming increasingly popular in modern organizations. Thanks to integration with Large Language Models (LLMs) and automation workflows, their capabilities go far beyond simple autoresponders.
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Speech analytics software, powered by Large Language Models (LLMs), is a powerful tool designed to support key business objectives in the contact center environment. By analyzing the content of conversations between agents and customers — both in real time and retrospectively — organizations can achieve measurable improvements in operational efficiency, service quality, regulatory compliance, and business strategy.
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Personalized communication
AI analyzes data from contact history, purchases, and user preferences to deliver personalized messages, recommendations, and offers tailored to each customer’s needs.
Prediction of customer needs and issues
By analyzing behaviors and contact patterns, AI can predict potential customer issues (e.g., risk of churn, need for technical support), enabling fast and proactive responses.
Data collection and analysis of evolving customer expectations
AI processes data from conversations, feedback, and interactions to identify emerging needs, trends, and recurring suggestions — providing valuable insights for product, marketing, and strategy teams.
Our AI solutions — including chatbots, voicebots, and mailbots — can be deployed in both cloud-based and on-premises environments, depending on the client's organizational and regulatory requirements. This flexibility allows for full alignment of the system architecture with institutional security policies, including strict data protection regulations.
In on-premises deployments, all data is processed locally, without the need to transfer it to external cloud service providers. This approach significantly increases control over sensitive information and ensures full compliance with both GDPR and internal security policies. In cloud-based scenarios, we work exclusively with certified providers who guarantee the highest data protection standards, including encryption, data segmentation, and access control.
Additionally, our solutions undergo a multi-stage testing process — both technical and functional — to detect potential vulnerabilities and unwanted model behavior. We also conduct regular ethical reviews, which include the assessment of risks related to algorithmic bias, model transparency, and the impact of AI-driven decisions on end users.
In this way, we build systems that not only enhance automation and efficiency but also ensure security, transparency, and legal compliance — which is especially critical in the public sector and other sensitive industries.
Sales Director
Email: magdalena.pisarczyk@yarrl.pl
Phone: +48 601 416 600
