Aderant builds intelligent ticket triage with Amazon Nova

Every organization grappling with an influx of inbound requests can now significantly streamline their support and operational workflows through advanced automation. The article details how Aderant, a software provider, developed an intelligent ticket triage system using Amazon Nova Lite via Amazon Bedrock. This system automates the critical, labor-intensive steps of gathering context, classifying tickets, routing them to the correct teams, and enriching them with relevant knowledge. Essentially, it transforms raw support requests into actionable, well-prepared tasks before a human ever touches them, dramatically reducing resolution times and improving efficiency. This innovation directly impacts anyone managing a service desk, an IT operations center, or customer support. Consider a mid-sized e-commerce company in Seattle, "Cascade Retail," receiving thousands of customer inquiries daily about orders, returns, and product issues. Instead of manual sorting, an intelligent triage system could instantly classify a "missing item" ticket, pull up the customer's order history from their database, suggest relevant shipping policies, and route it directly to the logistics team with all necessary information, reducing resolution time from hours to minutes. Similarly, an internal IT department at a financial firm in New York City, "Wall Street Capital," dealing with hundreds of software access requests and hardware issues, could automatically categorize a "password reset" request, confirm user identity, and route it to the appropriate identity management system for automated resolution or, for a more complex "new laptop setup," pre-populate a procurement form and assign it to the hardware team. An independent SaaS founder based in Austin, "TaskFlow," offering project management software, could use this to automatically prioritize bug reports from feature requests, attach relevant logs from their monitoring systems, and assign them to the correct development sprint, freeing up precious developer time. To explore this for your own operations, consider one common type of inbound request your team receives daily. Set up a basic experiment where you feed 50 such requests, stripped of sensitive data, into a large language model (LLM) through a development playground. Instruct the model to identify the core issue, suggest the appropriate team for resolution, and list three pieces of information typically needed to resolve it. Compare the time and accuracy of this automated process against your current manual approach.