Context Windows brings together credible AI case studies from the open web,
so you can pick and prioritise the use cases that are already working.
Real-world implementations from
Ideas from your team, no outside signal → Best guess prioritization
Demos that impress, outcomes that don't
Ideas from your team, no outside signal
Best guess prioritization
Demos that impress, outcomes that don't
Your shortlisted ideas, validated against 2,500+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,500+ real implementations
Prioritise by what's already paying off
Measurable ROI and competitive edge
Use case intelligence lets you see the winners,
so you can be in the 5%
Context Windows brings together credible AI case studies from the open web,
so you can pick and prioritise the use cases that are already working.
Real-world implementations from
Ideas from your team, no outside signal → Best guess prioritization
Demos that impress, outcomes that don't
Ideas from your team, no outside signal
Best guess prioritization
Demos that impress, outcomes that don't
Your shortlisted ideas, validated against 2,500+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,500+ real implementations
Prioritise by what's already paying off
Measurable ROI and competitive edge
Use case intelligence lets you see the winners,
so you can be in the 5%
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Thousands of project models across disconnected systems made every query a hunt. Natural language in Teams now surfaces answers immediately.
Scattered data made AI features impossible to build. Unifying it unlocked search that understands traveler intent, not just keywords.
Paper-and-highlighter compliance checks ate 20–30 min per shift across thousands of locations. A photo now does it in minutes.
Deep research capped at 12 voices; broad surveys flattened stories to checkboxes. Claude now tags sentiment across 300 workers at once.
37M product listings, and shoppers navigated alone. Now Lu guides the whole journey—search to checkout—inside WhatsApp.
AI agents that engage website visitors and inbound prospects 24/7 — qualifying interest, scoring intent, and booking meetings with sales reps automatically.
AI agents that autonomously handle customer requests — processing refunds, modifying accounts, making bookings, and resolving issues without human intervention.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
AI-assisted creation of written, visual, and multimedia content across marketing, communications, and publishing workflows.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Thousands of project models across disconnected systems made every query a hunt. Natural language in Teams now surfaces answers immediately.
Scattered data made AI features impossible to build. Unifying it unlocked search that understands traveler intent, not just keywords.
Paper-and-highlighter compliance checks ate 20–30 min per shift across thousands of locations. A photo now does it in minutes.
Deep research capped at 12 voices; broad surveys flattened stories to checkboxes. Claude now tags sentiment across 300 workers at once.
37M product listings, and shoppers navigated alone. Now Lu guides the whole journey—search to checkout—inside WhatsApp.
AI agents that engage website visitors and inbound prospects 24/7 — qualifying interest, scoring intent, and booking meetings with sales reps automatically.
AI agents that autonomously handle customer requests — processing refunds, modifying accounts, making bookings, and resolving issues without human intervention.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
AI-assisted creation of written, visual, and multimedia content across marketing, communications, and publishing workflows.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.