30+ platforms, scattered metadata, engineers manually pulling data for every leadership question. Now one Genie Agent answers in natural language.
A cloud-based enterprise platform for HR and finance whose AI platforms and SRE team support more than 30 AI and data platforms, handling roughly 7,000 to 8,000 incidents a year across about 39 business services.
Each platform kept its own metadata in its own place, so every leadership question about SLAs, uptime, incidents or change requests meant engineers...
Cloud platform for enterprise human resources, finance, and business planning.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Workday's Operations dashboards is part of this use case:
Related implementations across industries and use cases
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Campaign variants took 2 days; no shared customer view. AI now orchestrates audience, journeys, and creative while marketers steer outcomes.
On-chain AI agents took weeks of stitching and ran trapped on local machines. BNB Agent Studio ships them globally in 15 minutes.
AI scaling fast with no visibility into model costs or performance. Teams now catch runaway spend and failing models before deliveries slip.
AI costs scattered across teams with no owner, no baseline. A single view surfaced the true scale—two-thirds was labor, not compute.
30+ platforms, scattered metadata, engineers manually pulling data for every leadership question. Now one Genie Agent answers in natural language.
A cloud-based enterprise platform for HR and finance whose AI platforms and SRE team support more than 30 AI and data platforms, handling roughly 7,000 to 8,000 incidents a year across about 39 business services.
Each platform kept its own metadata in its own place, so every leadership question about SLAs, uptime, incidents or change requests meant engineers...
Cloud platform for enterprise human resources, finance, and business planning.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Workday's Operations dashboards is part of this use case:
Related implementations across industries and use cases
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Campaign variants took 2 days; no shared customer view. AI now orchestrates audience, journeys, and creative while marketers steer outcomes.
On-chain AI agents took weeks of stitching and ran trapped on local machines. BNB Agent Studio ships them globally in 15 minutes.
AI scaling fast with no visibility into model costs or performance. Teams now catch runaway spend and failing models before deliveries slip.
AI costs scattered across teams with no owner, no baseline. A single view surfaced the true scale—two-thirds was labor, not compute.