Jochem

Jochem.ai is an internal AI assistant specifically engineered for customer service teams, developed to streamline support operations. Its core value lies in acting as a real-time knowledge companion for agents, directly answering customer inquiries during live interactions. This reduces the time agents spend searching for information and minimizes the frequency with which they need to escalate complex issues to team leads or supervisors, thereby accelerating resolution times and improving both agent confidence and customer satisfaction.

Key features: The tool leverages a self-improving knowledge base that continuously learns from human-provided answers and corrections, ensuring its information remains accurate and up-to-date. It provides instant, context-aware responses to a wide range of customer care questions directly within the agent's workflow interface. The system can handle common queries about policies, procedures, product details, and troubleshooting steps. It also includes analytics to track which questions are asked most frequently and identify potential gaps in the knowledge base, enabling proactive improvements to training materials and support documentation.

What makes Jochem unique is its focus on being an internal tool that augments human agents rather than replacing them, designed to integrate seamlessly into existing customer service platforms like Zendesk or Salesforce Service Cloud. It operates by processing natural language queries from agents and retrieving the most relevant, approved answers from its curated knowledge base. The self-learning mechanism means that every time a human agent provides a better answer or corrects an AI suggestion, the system incorporates that feedback, becoming more precise over time without requiring manual knowledge base overhauls by administrators.

Ideal for customer support departments in e-commerce, SaaS companies, telecommunications, and any business with a medium to high volume of repetitive customer inquiries. Specific use cases include onboarding new support agents who can achieve proficiency faster with AI guidance, handling peak support times where speed is critical, and ensuring consistent, policy-compliant answers are given across a global or distributed support team. It is particularly valuable for organizations aiming to reduce average handle time (AHT) and improve first-contact resolution (FCR) metrics without compromising on answer quality.