AI that actually understands your data plane
Claude agents, retrieval, and automation grounded in the Cassandra, Kafka, and operational-data systems you already run. This is the work we are uniquely set up to deliver.
Scope of engagement
Most enterprise AI projects stall at the boundary between the model and the operational reality it's supposed to help with. This is the engagement where we cross that boundary.
- Retrieval over operational data: metrics, logs, topics, query plans, and audit streams.
- Claude-driven runbooks that execute against live Cassandra and Kafka estates, with human-in-the-loop approvals.
- Streaming enrichment: Claude-backed transformations applied inline on Kafka topics.
- Alert triage and root-cause reasoning agents wired into existing observability.
- Evaluation harnesses specific to data-platform operations, not generic benchmarks.
- Integration with AxonOps control-plane data for domain-aware retrieval and tooling.
This is the service where our DNA pays off
AxonOps has spent years building a control plane for Cassandra and Kafka. That experience is the difference between an AI engagement that produces a polished demo and one that ships a system your on-call engineers trust. We know where operational data is noisy, where metrics lie, where audit trails go stale, and what happens when an agent recommends the wrong repair. That hard-won context is baked into every engagement in this practice.
A predictable path from scope to running system
Map the data plane
Inventory of operational data sources, access patterns, sensitivity, and retention. This is the foundation for every later decision.
Scope the agent or pipeline
Pick a specific, measurable use case: incident triage, lag RCA, repair advisory, schema migration review. Define success.
Build and evaluate
Implement agents, retrieval, and tool use. Measure against a domain-specific evaluation set before production.
Operate and iterate
Deploy with observability, drift monitoring, cost control, and a feedback loop for the humans the agent works alongside.
What we build with our clients
Agents that understand your estate
Claude agents grounded in your schemas, topology, metrics, and runbooks instead of generic documentation.
Measurably shorter MTTR
Incident triage and reasoning assistance that compresses detection and resolution time for your actual on-call flows.
Automation without regret
Guardrailed, auditable actions with explicit approval steps where they matter. No opaque autonomous changes.
Common questions
Do we need AxonOps as a product to use these services?
No. Engagements work with whatever Cassandra, Kafka, or observability stack you run today. AxonOps customers benefit from deeper integration, but it is not a prerequisite.
Which Claude models do you typically use?
Retrieval and routing tasks usually run on Haiku or Sonnet. Reasoning over complex incidents lands on Sonnet or Opus. Model choice is part of scoping, not a default.
How do you avoid letting an agent make destructive changes?
By separating read-only reasoning from write actions, requiring explicit approvals on mutating tools, logging every tool call, and evaluating against failure-mode scenarios before production.
Can this run on-premise or in our own cloud account?
Yes. Most engagements deploy into your own environment, with your own Anthropic or cloud-provider AI account. BYOAI is the default pattern for regulated clients.
How do you measure whether an agent is actually good?
We build an evaluation set drawn from your real incidents and operations, and measure accuracy, calibration, and operational impact before anything goes live.
Start a conversation
Tell us about the system you're building or the decision you're trying to make. We'll match you with a specialist.