Aderant replaced manual ticket triage with a serverless workflow that calls Amazon Nova Lite via the Bedrock Converse API, automatically gathering context, classifying, and routing support tickets. Practitioners care because the change reduces repetitive investigation time, delivers high routing accuracy, and does so at a sub‑$30 monthly cost while staying within the existing AWS security model.
From Manual Investigation to Autonomous Triage
The SierraOps team previously handled 34–40 tickets each week, spending 15–25 minutes per ticket to locate client details, search documentation, and decide ownership. By introducing an hourly Lambda function triggered by EventBridge, the new process pulls unassigned tickets from Jira, enriches them with data from Athena, Confluence, and SharePoint, and sends the assembled payload to Amazon Nova Lite. The model returns a structured classification and suggested next steps, which the Lambda then uses to reassign tickets, post updates, and notify the appropriate Microsoft Teams channel. Low‑confidence results are routed to a human reviewer.
Key Architectural Elements
- Serverless orchestration: A single
aws lambdafunction runs on a schedule defined ineventbridge, eliminating the need for dedicated servers. - Data enrichment: Context is collected from
jira,athena,confluence, andsharepointbefore model invocation. - Model integration: Amazon Nova Lite is accessed through the Bedrock Converse API, leveraging native IAM permissions and the AWS SDK.
- Observability: Operational metrics, including confidence scores, misroutes, and latency, are published to
cloudwatchfor continuous monitoring. - Human‑in‑the‑loop guardrails: A confidence threshold determines whether routing is automatic or requires manual review.
Operational Impact and Cost
During the first 2.5 weeks of production (June 30 – July 17 2026), the system processed 109 tickets with roughly 96 % routing accuracy. Aderant estimates a recovery of 8–14 engineering hours per week, translating to a tangible reduction in toil. The total operating expense stayed under $30 per month, with Bedrock inference costs below $1 per month, confirming the economic viability of applying a large‑language model to routine tickets.
Security and Governance Considerations
The workflow relies on IAM policies to grant the Lambda function read access to Jira, Athena, Confluence, and SharePoint, and invoke Bedrock. Because the model runs within the same AWS account, there is no external data egress beyond the Bedrock service call, preserving the existing security perimeter. Practitioners should review IAM role scopes to ensure least‑privilege access and monitor CloudWatch logs for any unexpected data handling patterns.
Related CloudNinjas coverage: AWS.
What This Means For Practitioners
Adopting a similar pattern can automate repetitive ticket triage without sacrificing control: use a scheduled Lambda, pull relevant data sources, invoke a Bedrock model, and enforce confidence‑based routing. Evaluate model cost versus ticket volume, define clear confidence thresholds, and instrument metrics in CloudWatch to iterate on prompt design and routing logic. Finally, verify IAM permissions are tightly scoped to the data sources involved, and establish a review cadence for low‑confidence cases to maintain accuracy over time.
