Admins got the alert but not the answer—four fragmented systems to comb for hours. An AI agent now delivers root cause and fix in minutes.
A two-time Gartner Magic Quadrant Leader in enterprise data protection, trusted by 75 of the Fortune 500, managing hundreds of petabytes of backup storage and tens of millions of backups across hybrid and multi-cloud environments.
Operational, backup, file, and identity data were scattered across separate databases, custom APIs, file logs, and backup storage with no unified...
“Customers would receive an observability alert and then have to work out what to do next. We wanted to connect that context and give customers actionable recommendations.”
SaaS platform for cloud data protection, backup, and cyber resilience.
Cloud computing platform and on-demand infrastructure services.
Druva's Incident troubleshooting is part of this use case:
Related implementations across industries and use cases
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
Outage traces took weeks; security questionnaires consumed 150 hours each. Sentinel's agents now triage, resolve, and respond—autonomously.
Analysts spent days building reports from slow, siloed tools. Now, they ask a question and an AI agent instantly generates an accurate chart.
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Similarity search mixed up Q3 and Q4 earnings. Hybrid AI now anchors retrieval with keywords and learns from human fixes instantly.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Admins got the alert but not the answer—four fragmented systems to comb for hours. An AI agent now delivers root cause and fix in minutes.
A two-time Gartner Magic Quadrant Leader in enterprise data protection, trusted by 75 of the Fortune 500, managing hundreds of petabytes of backup storage and tens of millions of backups across hybrid and multi-cloud environments.
Operational, backup, file, and identity data were scattered across separate databases, custom APIs, file logs, and backup storage with no unified...
“Customers would receive an observability alert and then have to work out what to do next. We wanted to connect that context and give customers actionable recommendations.”
SaaS platform for cloud data protection, backup, and cyber resilience.
Cloud computing platform and on-demand infrastructure services.
Druva's Incident troubleshooting is part of this use case:
Related implementations across industries and use cases
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
Outage traces took weeks; security questionnaires consumed 150 hours each. Sentinel's agents now triage, resolve, and respond—autonomously.
Analysts spent days building reports from slow, siloed tools. Now, they ask a question and an AI agent instantly generates an accurate chart.
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Similarity search mixed up Q3 and Q4 earnings. Hybrid AI now anchors retrieval with keywords and learns from human fixes instantly.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.