AI Log Diagnosis Assistant
We build AI assistants that classify errors, explain root causes, retrieve similar historical cases, and suggest fixes for workflow and platform logs.
Problem
Operational friction this service addresses.
- Traditional alerting reports a failure but not the reason or next action.
- Long task logs hide the key error behind repeated framework output.
- Historical fixes are scattered across tickets, chat, and internal documents.
- Junior engineers depend on manual escalation for recurring incidents.
What we deliver
Practical outputs your engineering team can use.
Log ingestion and preprocessing
Error pattern taxonomy
Historical case library
LLM-based explanation and suggested fixes
Workflow metadata integration
Private deployment with feedback loop
Use cases
Typical project scenarios.
- Airflow DAG failure diagnosis
- DolphinScheduler task failure diagnosis
- Spark, Flink, Hive, DataX, Python, Shell, and Kubernetes pod log analysis
- Ticketing or alerting integration for incident workflows
Technical approach
How the work is structured.
Step 1
Collect representative logs and workflow metadata.
Step 2
Normalize task, workflow, environment, and error fields.
Step 3
Build an error classification taxonomy and retrieval layer.
Step 4
Generate explanations, fixes, responsible components, and risk levels.
Step 5
Evaluate against real cases and improve with human feedback.
Example deliverables
Artifacts and handover materials.
- Working web interface
- Diagnosis API endpoint
- RAG knowledge base
- Admin configuration
- Deployment guide
- Evaluation report
Engagement model
Designed for staged adoption.
- 2-4 week prototype
- 4-8 week production pilot
- Maintenance and model evaluation
FAQ
Common questions.
Do logs need to leave our environment?+
No. The assistant can be designed for private deployment with controlled access to logs, tickets, and internal documents.
How do you measure whether diagnosis quality improves?+
We evaluate against historical incidents, recurring failure patterns, engineer feedback, and troubleshooting time reduction.
Start with AI Log Diagnosis.
Share your current workflow platform, failure examples, and operational bottleneck. We will help identify the lowest-risk starting point.