A decade of tool growth left regional procurement scattered, invisible to the center. One platform, bought not built, surfaced them all.
A global ride-hailing and delivery platform that built its procurement function from scratch over a decade, accumulating 10 separate tools and expanding procurement staff to 20-plus markets before consolidating to regional hubs.
The tool-by-tool growth left procurement fragmented: when the team mapped regional processes, they found staff in different regions using tools and...
“If you get it wrong, your job could be on the line.”
Mobility and delivery platform for ride-hailing, food, and freight logistics.
AI-powered procurement orchestration platform for enterprise intake-to-pay.
Uber's Procurement operations is part of this use case:
Related implementations across industries and use cases
Manual price benchmarking consumed hours per case. AI agents now analyze costs and draft negotiation emails in seconds.
SThree people, three different AI answers. Grounded data sources fixed that — finance now has years of vendor spend at its fingertips.
The AI bill arrived as one number — nowhere to cut. Broken into six terms and stripped of 70K idle tokens, it became six independent levers.
9,000 POs handled through fragmented manual steps. End-to-end AI agents cut quote comparison from 30 minutes to 3.
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Reconstruction cost capped how much fleet data Li Auto could simulate. A unified camera-LiDAR model halved cost and doubled scene coverage.
Fragmented support caused frustrating handoffs. Live AI translation now lets a small English-speaking team handle 14 languages instantly.
Fragmented data and manual workflows slowed care. Now, AI helps staff optimize surgical scheduling and physicians diagnose rare diseases.
QC simulations ran 15 hours and reset from scratch on every change. Digital twins cut that to 3.6 seconds.
A decade of tool growth left regional procurement scattered, invisible to the center. One platform, bought not built, surfaced them all.
A global ride-hailing and delivery platform that built its procurement function from scratch over a decade, accumulating 10 separate tools and expanding procurement staff to 20-plus markets before consolidating to regional hubs.
The tool-by-tool growth left procurement fragmented: when the team mapped regional processes, they found staff in different regions using tools and...
“If you get it wrong, your job could be on the line.”
Mobility and delivery platform for ride-hailing, food, and freight logistics.
AI-powered procurement orchestration platform for enterprise intake-to-pay.
Uber's Procurement operations is part of this use case:
Related implementations across industries and use cases
Manual price benchmarking consumed hours per case. AI agents now analyze costs and draft negotiation emails in seconds.
SThree people, three different AI answers. Grounded data sources fixed that — finance now has years of vendor spend at its fingertips.
The AI bill arrived as one number — nowhere to cut. Broken into six terms and stripped of 70K idle tokens, it became six independent levers.
9,000 POs handled through fragmented manual steps. End-to-end AI agents cut quote comparison from 30 minutes to 3.
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Reconstruction cost capped how much fleet data Li Auto could simulate. A unified camera-LiDAR model halved cost and doubled scene coverage.
Fragmented support caused frustrating handoffs. Live AI translation now lets a small English-speaking team handle 14 languages instantly.
Fragmented data and manual workflows slowed care. Now, AI helps staff optimize surgical scheduling and physicians diagnose rare diseases.
QC simulations ran 15 hours and reset from scratch on every change. Digital twins cut that to 3.6 seconds.