Static recommendations couldn't parse mood or cricket stats for 500M viewers. Now they just ask, in 15+ languages.
An entertainment and live-sports streaming platform serving more than 500 million consumers, with as many as 72.5 million concurrent live-sports viewers at peak.
Content discovery had barely evolved in 25 years, and traditional recommendation systems couldn't capture the nuance of a viewer's mood or interpret...
“Content discovery is inherently visual, but combining those visual options with spoken preferences and conversation creates a powerful new paradigm.”
Media conglomerate operating television networks and digital streaming services.
AI research and deployment company developing generative models and tools.
JioStar's Content discovery is part of this use case:
Related implementations across industries and use cases
Vague feedback on unrecorded streams slowed fixes. AI now diagnoses live issues instantly, lifting user satisfaction 30%.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
Rigid scripts left complex migrations manual. Now, a background agent turns natural language prompts into verified, merged pull requests.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Classrooms with 1:30 ratios left students behind. Now, 350M monthly queries route to specialized models for personalized tutoring.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Answering 'mediums or hards?' once meant pulling an engineer for minutes. Now the whole field's tire feedback surfaces in a chat query.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.
Static recommendations couldn't parse mood or cricket stats for 500M viewers. Now they just ask, in 15+ languages.
An entertainment and live-sports streaming platform serving more than 500 million consumers, with as many as 72.5 million concurrent live-sports viewers at peak.
Content discovery had barely evolved in 25 years, and traditional recommendation systems couldn't capture the nuance of a viewer's mood or interpret...
“Content discovery is inherently visual, but combining those visual options with spoken preferences and conversation creates a powerful new paradigm.”
Media conglomerate operating television networks and digital streaming services.
AI research and deployment company developing generative models and tools.
JioStar's Content discovery is part of this use case:
Related implementations across industries and use cases
Vague feedback on unrecorded streams slowed fixes. AI now diagnoses live issues instantly, lifting user satisfaction 30%.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
Rigid scripts left complex migrations manual. Now, a background agent turns natural language prompts into verified, merged pull requests.
Data scattered across systems forced generic content. Now, AI agents surface personalized stats and videos via chat during live matches.
Classrooms with 1:30 ratios left students behind. Now, 350M monthly queries route to specialized models for personalized tutoring.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Answering 'mediums or hards?' once meant pulling an engineer for minutes. Now the whole field's tire feedback surfaces in a chat query.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.