Aderant’s CloudOps AI Win: Amazon Quick and AI Agents Cut Search Time 90%

AI for CloudOps: How Aderant Cut Search Time by 90% with Amazon Quick

  • Problem: Fragmented knowledge across Confluence, SharePoint, Git, Jira, Teams and dashboards slowed incident response and documentation.
  • Solution: Added Amazon Quick, an AI search-and-automation layer, to unify sources and run Quick Flows (with human review).
  • Headline results: Cross-platform search down ~90%, client-history research down ~95%, documentation time fell ~75%, and documentation throughput tripled.
  • Leader takeaways: Combine AI agents with workflow automation, enforce strict data boundaries, and require human-in-the-loop verification for production trust.

The problem: fragmented knowledge costs engineers hours

When CloudOps teams must stitch together context from six or more systems, mean time to resolution (MTTR) inflates. Engineers spend the first portion of every incident doing archival research—searching Confluence pages, Git commits, Jira tickets, Teams chats and operational dashboards—before they can even start debugging. That’s costly talent time and delayed customer impact.

The solution: AI agents + automated flows

Aderant’s 38-person Cloud Engineering team layered Amazon Quick across their operational stack to create a single discoverable surface for internal knowledge and telemetry. Quick Research surfaced relevant context, Quick Flows automated routine triage and documentation steps, and Quick Spaces organized team knowledge. Integrations included Confluence, SharePoint, Git repositories, Jira, Microsoft Teams, QuickSight dashboards and three MCP servers for meeting transcripts.

Authentication and access used Okta SSO (single sign-on) and AWS IAM roles. CloudWatch alarms and tenant health metrics were fed into Quick for trend analysis. Importantly, the system only analyzes internal CloudOps and AWS infrastructure data; it does not access client application or business data.

Implementation timeline and rollout

  • Pilot: October 2025
  • Chrome extension + full deployment: November 2025
  • Broader expansion and Support Helper pilot: through February 2026

Adoption was rapid—CloudOps Helper reached ~95% active use among the 38 engineers; the Support Helper pilot expanded adoption to ~80% and added 86 users. Quick uptime stayed above 99% during this period.

Results: measurable time savings and higher throughput

  • Cross-platform search: from ~30–45 minutes to ~3–5 minutes (~90% reduction).
  • Client history research: from ~2–4 hours to ~2–3 minutes (~95% reduction).
  • Documentation creation: fell ~75% (≈60 minutes → ≈15 minutes); some workflows showed 75–85% gains.
  • Root cause analysis: accelerated by ~60–70%.
  • Throughput: documentation output increased roughly 200%; backlog dropped from >40 articles to <10.

Metrics were collected from usage logs, ticket timestamps and before/after time tracking reported by the team during pilot and early production phases.

“Faster search was only step one — the real change came when AI search was combined with workflow automation.”

An incident vignette: actionable timelines in minutes

During a domain trust failure that interrupted authentication, the Quick agent pulled meeting transcripts from MCP servers, ticket history from Jira, and relevant runbook snippets to synthesize a chronological troubleshooting timeline and recommended next steps. What previously required hours of manual compilation became an engineer-reviewed timeline in minutes—human-in-the-loop, not human-out-of-the-loop.

“What used to take hours of manual research now completes in minutes, letting engineers focus on fixes rather than hunting for context.”

Governance, security, and hallucination controls

Operational trust hinged on three design choices:

  • Clear data boundaries: the AI analyzes only internal CloudOps and AWS infrastructure signals; client app/business data is excluded.
  • Identity and auditability: Okta SSO + AWS IAM control who can query which sources; audit logs record queries and actions.
  • Human-in-the-loop safeguards: all automated drafts and critical recommendations require engineer review; duplicate detection and provenance tagging reduce risk of misinformation.

Additional guardrails recommended and applied include confidence thresholds (flagging low-confidence responses), provenance links to original documents, and staged rollout for any fully automated flows (e.g., start with draft-only, then escalate to ticket creation with confirmation). These controls mitigate hallucination risk and keep operators accountable for decisions.

Practical ROI example (conservative)

To illustrate potential financial impact, consider a conservative scenario built from the reported gains:

  • 38 engineers
  • Conservative weekly time saved per engineer: 3 hours (from faster research and less documentation time)
  • Monthly hours saved: 38 × 3 × 4 = 456 hours
  • Fully-loaded hourly cost (conservative): $80/hr → monthly savings ≈ $36,480; annual ≈ $437,760

This is an illustrative worked example. Aderant’s reported operational metrics underpin these savings; any organization should instrument its own baseline and pilot measurements to produce accurate TCO and ROI figures that account for licensing, storage, and maintenance costs.

Adoption and change management

High usage (~95% among CloudOps) came from tight scope and early wins. Steps that drove adoption:

  • Start small: limit to internal ops data and a single team
  • Ship a Chrome extension and immediate value (fast search) to build habit
  • Design low-friction Flows (notes, draft docs) that reduce daily friction
  • Empower champions to validate content and act as early reviewers

Tradeoffs, vendor risks, and long-term maintenance

Embedding Amazon Quick and its connectors accelerates time-to-value but deepens dependence on a single vendor. Mitigation strategies include regular exports of knowledge artifacts, maintaining canonical runbooks in source-controlled systems, and building abstraction layers for connectors where feasible. Knowledge staleness requires ownership: designate doc owners, schedule periodic reviews, and use automated staleness alerts.

A 30–60 day pilot playbook for leaders

  • Week 0–2: define scope (internal ops data only), map sources, configure Okta + IAM policies, enable audit logging.
  • Week 2–4: onboard 1–2 teams, deploy Quick Chrome extension, configure Quick Research and a single Quick Flow (draft docs or meeting notes).
  • Week 4–8: instrument metrics (search time, doc time, MTTR), run feedback sessions, enable duplicate detection and provenance tags, expand support pilot.
  • Acceptance criteria: measurable time savings, ≥75% active use in pilot team, no data boundary violations, audit logs validated.

Key metrics at-a-glance

  • Cross-platform search: ~90% faster
  • Client history research: ~95% faster
  • Documentation time: ~75% faster; output ~200% increase
  • CloudOps Helper adoption: ~95% active use
  • Quick uptime: >99%

Final perspective for decision-makers

AI agents deliver the greatest operational leverage when they do more than retrieve documents: they must synthesize context, automate low-risk workflows, and keep humans firmly in control of decisions. Aderant’s deployment of Amazon Quick demonstrates that careful scoping, strong identity controls, and staged automation produce rapid, measurable gains for CloudOps. For executives evaluating AI for business, the right pilot design—limited data surface, instrumented measurement, and human verification—turns promising technology into durable operational capability.

“The bot only inspects internal CloudOps and AWS infrastructure data; it does not access client application or business data.”

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