One AE held the team's institutional knowledge—90% of his day in Slack. A digital double in his voice freed him to sell.
A software company whose institutional knowledge was fragmented across Gong, Salesforce, and Slack, with new hires depending on a handful of long-tenured employees for everything from product context to process guidance.
The first account executive was spending 90% of his day answering internal Slack questions, leaving no capacity for actual sales work. Expert...
“I have the opportunity to think more about the art of marketing and have my data engineer too—my Notion Custom Agent.”
Inference platform for deploying and scaling machine learning models in production.
A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team.
Baseten's Expert Q&A is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Onboarding across four platforms required weeks of shadowing. Now, an AI assistant instantly answers Slack queries so new hires self-serve.
Teams tab-hopped to find scattered info. AI now synthesizes answers instantly, getting new hires client-ready in their first month.
Teams tab-hopped to find scattered info. AI now synthesizes answers instantly, getting new hires client-ready in their first month.
Critical info was buried in Drive. Notion AI now surfaces answers instantly and drafts outreach, helping sales double weekly booked meetings.
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.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
One AE held the team's institutional knowledge—90% of his day in Slack. A digital double in his voice freed him to sell.
A software company whose institutional knowledge was fragmented across Gong, Salesforce, and Slack, with new hires depending on a handful of long-tenured employees for everything from product context to process guidance.
The first account executive was spending 90% of his day answering internal Slack questions, leaving no capacity for actual sales work. Expert...
“I have the opportunity to think more about the art of marketing and have my data engineer too—my Notion Custom Agent.”
Inference platform for deploying and scaling machine learning models in production.
A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team.
Baseten's Expert Q&A is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Onboarding across four platforms required weeks of shadowing. Now, an AI assistant instantly answers Slack queries so new hires self-serve.
Teams tab-hopped to find scattered info. AI now synthesizes answers instantly, getting new hires client-ready in their first month.
Teams tab-hopped to find scattered info. AI now synthesizes answers instantly, getting new hires client-ready in their first month.
Critical info was buried in Drive. Notion AI now surfaces answers instantly and drafts outreach, helping sales double weekly booked meetings.
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.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.