SafetyCulture uses Glean to cut search times by 50% and become AI-first with Agent Builder
During rapid growth, a 50% increase in engineering staff and new tools created organizational and productivity challenges at SafetyCulture; an internal survey identified knowledge search as the biggest culprit, with data spread across multiple applications and knowledge siloed. Engineers were spending up to four hours a week manually searching for content.
Implementing Glean immediately cut search times by 50% and it became the company's default native search solution.
Agents built with Agent Builder save engineers 30–40 minutes per performance review, and cover use cases from debugging to marketing intelligence.
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Frequently asked questions
What did this team achieve with this AI workflow?
Implementing Glean immediately cut search times by 50% and it became the company's default native search solution.
What tools did this team use?
Glean, Agent Builder, Jira, Github.
What results were reported?
Hours spent searching per week: 50%; engineer manual search time before Glean: up to four hours a week; Time saved per employee per week on information searching: 1.5 hours per week; Employee adoption rate: 80% (source-reported, not independently verified).
How is this it support AI workflow structured?
Survey identifies search problem → Glean deployed as enterprise search → AI delivers contextualized results → Employees build custom agents → Agents deliver workflow outputs.