Context Windows brings together credible case studies on what companies are doing with AI, from across the open web. We compare their results so you can prioritise the use cases that are actually working.
NewConnect ChatGPT & Claude to this data — now available as API & MCP →
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 2,800+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,800+ 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%
Also available as API & MCP — bring the data into ChatGPT, Claude, or your own tools|API reference →
Context Windows brings together credible case studies on what companies are doing with AI, from across the open web. We compare their results so you can prioritise the use cases that are actually working.
NewConnect ChatGPT & Claude to this data — now available as API & MCP →
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 2,800+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,800+ 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%
Also available as API & MCP — bring the data into ChatGPT, Claude, or your own tools|API reference →
Clinical trials generate duplicated content across dozens of global sites. Agents now handle it; 30,000 BMS employees focus on the science.
30 minutes per letter, 1,000+ sent per rep each year. AI now drafts and transcribes; no message reaches a customer without a human review.
Lost parts sales sent teams manually piecing together the root cause from scattered data. Five subagents now surface the full picture.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Outside counsel was the default for time or expertise gaps. Harvey brought complex matters in-house — same team, no new headcount.
Classifying 2,000 vendors under DORA meant paying lawyers and 10–15 manual reviews weekly. AI now routes only 1–2 edge cases to humans.
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.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
Personalized experiences, recommendations, and outreach — AI that picks the offer, generates the message, and adapts in real time.
Clinical trials generate duplicated content across dozens of global sites. Agents now handle it; 30,000 BMS employees focus on the science.
30 minutes per letter, 1,000+ sent per rep each year. AI now drafts and transcribes; no message reaches a customer without a human review.
Lost parts sales sent teams manually piecing together the root cause from scattered data. Five subagents now surface the full picture.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Outside counsel was the default for time or expertise gaps. Harvey brought complex matters in-house — same team, no new headcount.
Classifying 2,000 vendors under DORA meant paying lawyers and 10–15 manual reviews weekly. AI now routes only 1–2 edge cases to humans.
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.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
Personalized experiences, recommendations, and outreach — AI that picks the offer, generates the message, and adapts in real time.