Aderant cuts search time 90% with unified AI knowledge platform
Aderant, a legal software provider, deployed Amazon Quick to unify search across six disconnected knowledge systems for its 38-person Cloud Engineering team supporting Expert Sierra, a cloud-based practice management platform. The implementation, completed in weeks rather than months, reduced manual search time from 30-45 minutes per task to 90 percent faster queries and accelerated documentation workflows by 75 percent. The success led to expansion to a Support Helper bot serving 86 additional team members by February 2026, demonstrating how AI-powered search and workflow automation can reduce operational friction in knowledge-heavy support environments.
TL;DR
- →Aderant deployed Amazon Quick to consolidate search across Confluence, SharePoint, Git, Jira, Teams, and QuickSight dashboards into a single natural language interface
- →Search time dropped 90 percent and documentation creation accelerated 75 percent, freeing engineers from information hunting to focus on problem-solving
- →Full CloudOps deployment completed in weeks using pre-built integrations and built-in security controls, avoiding months of custom development
- →Success with CloudOps team prompted expansion to Product Support organization, bringing Quick to 86 additional users by February 2026
Why it matters
This case demonstrates how enterprise AI search tools can address a persistent operational problem: knowledge fragmentation across multiple systems. Rather than building custom integrations or requiring engineers to manually search multiple dashboards, pre-built AI-powered search with natural language interfaces can consolidate access to institutional knowledge at scale. The speed of deployment and measurable impact on response times show that practical AI applications in operations are moving beyond pilots into production workflows.
Business relevance
For operators managing support teams or engineering organizations, this illustrates concrete ROI from AI tooling: reducing time spent on information retrieval directly translates to faster issue resolution and improved customer response times. The ability to deploy across multiple teams without extensive custom development or security rework lowers the barrier to adoption and makes the business case easier to justify. For software vendors serving regulated industries like legal, demonstrating data isolation and security controls is critical to customer trust.
Key implications
- →Pre-built integrations and managed security reduce deployment friction, allowing teams to realize AI benefits in weeks rather than months of engineering effort
- →Natural language search across fragmented knowledge systems can measurably improve operational efficiency in support and engineering roles where information discovery is a bottleneck
- →Successful pilots in one team create momentum for expansion, suggesting that initial deployments should be scoped to high-impact, measurable use cases that can drive adoption elsewhere in the organization
What to watch
Monitor whether Aderant expands Quick usage beyond CloudOps and Support to other functions, and whether similar patterns emerge in other vertical software providers serving knowledge-intensive industries. Watch for case studies on how AI search impacts customer satisfaction metrics and support ticket resolution times, as these will be key indicators of whether this approach scales beyond operational efficiency to customer-facing outcomes.
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