For the 38 cloud engineers supporting Aderant’s Expert Sierra legal practice management solution, the workday was once defined by a fragmented digital landscape. To resolve a single technical task, engineers were forced to manually navigate six distinct systems: Confluence, SharePoint, Git, Jira, Microsoft Teams, and QuickSight. This context-switching tax meant that simple information retrieval could consume 30 to 45 minutes per task, creating a significant bottleneck for a team managing complex, mission-critical infrastructure.
Integrating the Six-System Stack
In October 2025, Aderant launched a pilot program for CloudOps Helper, an AI-driven bot designed to unify these disparate data sources. Unlike traditional enterprise rollouts that often require months of custom development, Aderant utilized the Model Context Protocol (MCP) to bridge its existing systems. By November 2025, the company had completed a full system deployment, including a Chrome extension that allowed engineers to access the unified search interface directly within their browsers.
The integration strategy focused on layering AI over existing knowledge repositories rather than replacing them. By February 2026, the success of the CloudOps implementation led to the launch of Support Helper, extending the tool’s reach to 86 members of the product support organization. Throughout this expansion, Aderant maintained strict security protocols, utilizing Okta SSO and IAM to ensure the AI only accessed authorized infrastructure data. The system has since maintained an uptime of over 99%, cementing its role as a core component of the Expert Sierra support architecture.
The Mechanics of MCP and Workflow Automation
The shift from manual dashboard navigation to a unified interface relies on three MCP servers that allow the AI to interact with the six core knowledge systems. By using natural language queries, engineers can now pull data from Confluence, SharePoint, Git, Jira, Teams, and QuickSight simultaneously. This eliminates the need to manually cross-reference Jira tickets with Git commit logs or technical documentation.
Beyond search, Aderant implemented Amazon Quick Flows to automate documentation. This tool includes a duplicate content detection feature that prevents redundant entries, reducing the time required to create a knowledge base article from 60 minutes to 15 minutes. To maintain accuracy, the system employs a human-in-the-loop design, requiring engineers to review and approve AI-generated drafts before publication. Additionally, Amazon Quick Research analyzes bot usage patterns to identify frequently asked questions, while Amazon Quick Spaces integrates these insights back into the knowledge base. This creates a feedback loop where the system identifies documentation gaps and prompts engineers to fill them, effectively turning tacit knowledge into explicit, searchable assets.
Quantifiable Gains in Operational Efficiency
The impact of this integration is most visible in the reduction of operational friction. Before the deployment of Amazon Quick, engineers spent 30 to 45 minutes per task searching for information across the six-vendor stack. With the new unified interface, that time has dropped to 3 to 5 minutes, representing an efficiency gain of over 90%.
For high-load tasks, such as investigating client history during a network outage, the improvement is even more pronounced. Previously, reconstructing the context of a complex issue required hours of manually tracing tickets and meeting transcripts. Now, the system synthesizes this data into a chronological timeline in 2 to 3 minutes, a 95% reduction in time. This capability has improved the speed of root cause analysis by 60% to 70%. Furthermore, the automation of documentation has cleared the backlog of pending articles from 40 down to fewer than 10, while simultaneously increasing total knowledge production by 200%.
By capturing context immediately after a problem is solved, Aderant has successfully transformed its documentation process from a reactive chore into a proactive knowledge-sharing engine. The high adoption rate, with 95% active usage among the CloudOps team, confirms that the tool has become an essential part of the daily engineering workflow.
