Blog

CrushBank's SmartArchive enables businesses to efficiently preserve and access critical IT support data from legacy systems, offering a cost-effective, scalable solution that eliminates expensive licensing fees while ensuring unlimited user access to searchable historical information.

CrushBank's SmartClassifier automates the classification of service tickets, optimizing data management, and offering customizable models to enhance efficiency and insights while turning chaotic ticketing processes into structured, actionable workflows.

Utilizing AI-driven search capabilities, automation, and integration, CrushBank Neuro enhances efficiency, reduces onboarding time, and empowers teams to manage information more effectively—ultimately transforming how IT services operate in a cost-effective manner.

Revolutionize your business efficiency with CrushBank Neuro, an AI-driven solution that simplifies data retrieval across all departments, transforming unsearchable information into actionable insights.

As Managed Services Providers (MSPs) increasingly adopt generative AI technologies like Large Language Models (LLMs), establishing robust AI governance becomes essential to mitigate risks and ensure responsible, ethical usage across their operations.

As Artificial Intelligence (AI) evolves at a rapid pace, grasping the core concepts that underpin this dynamic field is essential. This guide explores 50 key AI terms, designed to enhance your understanding and bridge any gaps in your knowledge.

Relying on a general-purpose AI for ticket classification in Managed Service Provider environments can be a costly mistake, as accurate classification is essential to ensure efficient workflows and meaningful analytics in IT support.

While generative AI has the potential to revolutionize operations for Managed Service Providers, many projects face significant challenges; CrushBank offers a solution by leveraging a private data lake and IBM watsonx to enhance service delivery and improve response accuracy.

CrushBank CEO Evan Leonard emphasizes that the success of AI initiatives relies on high-quality, diverse data, highlighting the need for strong data management practices to unlock AI's potential and maintain a competitive edge.