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 →
India's top institutes were limited to 500 students per cohort by campus capacity. Adaptive AI tutoring and hybrid delivery broke that wall.
Every AI workflow meant engineering approval gates from scratch—employees held back from delegating. Cowork shipped with them baked in.
Caregivers quit to reapply externally rather than transfer. RITA now handles 35,000 hires and transfers through one interface.
Exceptions, claims, disputes sat beyond automation's reach—too complex for bots, too routine for engineers. Custom agents handle them.
Metric definitions had splintered; six analysts handled all requests. A Claude agent now routes 30+ users to consistent, SQL-backed answers.
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
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.
India's top institutes were limited to 500 students per cohort by campus capacity. Adaptive AI tutoring and hybrid delivery broke that wall.
Every AI workflow meant engineering approval gates from scratch—employees held back from delegating. Cowork shipped with them baked in.
Caregivers quit to reapply externally rather than transfer. RITA now handles 35,000 hires and transfers through one interface.
Exceptions, claims, disputes sat beyond automation's reach—too complex for bots, too routine for engineers. Custom agents handle them.
Metric definitions had splintered; six analysts handled all requests. A Claude agent now routes 30+ users to consistent, SQL-backed answers.
Robotics labs capped at 6–7 training runs/day on desktop GPUs. Shared campus compute lifted that to 100+ runs daily.
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.