18 teams, siloed data, cargo checked flight-by-flight. Now one shared workspace turns bookings and campaigns into real-time decisions.
The country's largest airline, carrying more than 45 million passengers across six continents on over 190 routes.
Eighteen teams each had their own data, tools, and starting point spread across systems never built to talk to each other, despite no shortage of...
“There was no shortage of AI ideas. There was a shortage of actual data foundations to enable that.”
Flag carrier airline for domestic and international passenger and cargo travel.
AI and machine learning platform for enterprise data science and analytics.
Air Canada's Campaign and cargo analytics is part of this use case:
Related implementations across industries and use cases
Data siloed across tools, finance started with one schema and no data. Now non-technical users query 10K food items in 2 minutes.
Analysts combed 40+ page PDFs by hand for emissions data, delaying reports up to a week. Now an AI agent extracts it in minutes.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Managing 200TB of player data required heavy manual oversight. Now, serverless pipelines let scientists deploy models themselves.
Telemetry from 6M machines was trapped in internal silos. Unifying it into a shared platform lets customers deploy predictive AI.
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140 lawyers, 50 brands, contracts read one by one — complex portfolios shipped to outside counsel. Grid analysis eliminated the outsourcing.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
18 teams, siloed data, cargo checked flight-by-flight. Now one shared workspace turns bookings and campaigns into real-time decisions.
The country's largest airline, carrying more than 45 million passengers across six continents on over 190 routes.
Eighteen teams each had their own data, tools, and starting point spread across systems never built to talk to each other, despite no shortage of...
“There was no shortage of AI ideas. There was a shortage of actual data foundations to enable that.”
Flag carrier airline for domestic and international passenger and cargo travel.
AI and machine learning platform for enterprise data science and analytics.
Air Canada's Campaign and cargo analytics is part of this use case:
Related implementations across industries and use cases
Data siloed across tools, finance started with one schema and no data. Now non-technical users query 10K food items in 2 minutes.
Analysts combed 40+ page PDFs by hand for emissions data, delaying reports up to a week. Now an AI agent extracts it in minutes.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Managing 200TB of player data required heavy manual oversight. Now, serverless pipelines let scientists deploy models themselves.
Telemetry from 6M machines was trapped in internal silos. Unifying it into a shared platform lets customers deploy predictive AI.
A 15-year-old payroll macro accountants feared would break. Now AI agents book payroll and cut utility compiling from 3 days to 1 hour.
140 lawyers, 50 brands, contracts read one by one — complex portfolios shipped to outside counsel. Grid analysis eliminated the outsourcing.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.