AI Assistant for AxonOps

‍Background

AxonOps is a leading observability and operations platform for Apache Cassandra and Kafka, trusted globally to simplify the management of critical distributed databases. To further enhance developer productivity and reduce operational friction, AxonOps partnered with Digitalis.io—renowned for expertise in building and operating large-scale, distributed data platforms.

Together, the teams developed an AI-powered Expert Assistant, trained on both public resources and proprietary AxonOps operational knowledge. The goal: give engineering teams a real-time expert partner that accelerates problem-solving, optimizes performance, reduces overhead, and makes Kafka and Cassandra easier to operate at scale.

The Challenge

For many enterprises, working with Cassandra and Kafka introduces persistent challenges:

High complexity – clustering, scaling, and operational tuning require deep technical knowledge.‍

Time-consuming troubleshooting – identifying and resolving issues often eats into developer productivity.‍

Knowledge gaps – teams struggle with inconsistent access to experts or detailed troubleshooting guidance.

Operational overhead – monitoring and maintaining clusters at enterprise scale requires significant resources.

AxonOps wanted to go further than observability alone and provide clients with an AI-enabled expert assistant to help alleviate these common pain points.

The Solution: AI-Powered Expert Assistant

Working in partnership with Digitalis.io, AxonOps built and embedded a purpose-designed AI assistant into its platform. The solution combined AI-driven intelligence with battle-tested operational expertise, making Cassandra and Kafka management faster, easier, and more reliable.

Key Capabilities:

The AI assistant delivered a comprehensive knowledge base by combining AxonOps’ proprietary IP with public Apache documentation, enabling instant answers on topics such as CQL, Kafka driver configurations, compaction tuning, and best practices. It supported real-time troubleshooting by offering actionable recommendations for cluster issues and performance bottlenecks, while also generating tailored code snippets and optimizing queries based on workload patterns. Seamlessly integrated into the AxonOps Workbench, the assistant provided developers with direct access to insights alongside dashboards and real-time performance metrics. Over time, it continued to improve its accuracy and relevance through continuous learning driven by feedback from developer interactions.

Digitalis’ Role in Delivery:

Digitalis.io designed the knowledge training strategy to ensure the AI assistant’s outputs were closely aligned with real-world operational needs. Drawing on deep consulting experience across Cassandra, Kafka, and Elasticsearch, the team incorporated comprehensive enterprise use cases into the model. They then validated and fine-tuned the outputs to guarantee both technical accuracy and practical usefulness in day-to-day operations. By applying extensive consulting and managed services expertise, Digitalis.io ensured the assistant became an operationally reliable co-pilot for data engineers.

Implementation Process

The implementation process began with data aggregation, where both structured data such as logs and metrics and unstructured content like guides and troubleshooting resources were curated and combined from public and proprietary sources. Using this foundation, the team conducted AI model training, applying advanced natural language processing techniques to build a domain-specific knowledge model tailored to Cassandra and Kafka operations. Finally, the assistant was seamlessly integrated into the AxonOps Workbench, giving developers instant access to its capabilities within their existing workflows and daily operational environment.

Results / Outcomes

The AxonOps AI Assistant, powered by Digitalis expertise, delivered measurable impact for customers:

25 hours saved per developer per week on debugging and research tasks

40% faster project delivery by removing operational bottlenecks in dev cycles

30% increase in cluster efficiency through proactive AI-driven recommendations

50% faster developer onboarding, with new staff using the assistant to self-serve knowledge and learn best practices

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