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.
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 2,600+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,600+ 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 AI case studies from the open web,
so you can pick and prioritise the use cases that are already 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 2,600+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,600+ 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 →
Sensitive triggers flooded the queue; analysts cleared cases one at a time. Four agents now pre-screen each anomaly in parallel.
A small data team fielded every ad hoc query—sometimes hours, sometimes days. Colleagues now ask in plain English via Teams, in seconds.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Manual workflows slowed fight insight to a crawl. AI now delivers storylines on demand, freeing analysts to narrate, not wrangle.
Charting bled into evenings and personal time. When Summa required AI at every encounter, adoption jumped from 44% to 86% in four months.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
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.
Real-time fraud and identity verification that combines scoring models with AI reasoning over transaction context, customer history, and unstructured signals.
Sensitive triggers flooded the queue; analysts cleared cases one at a time. Four agents now pre-screen each anomaly in parallel.
A small data team fielded every ad hoc query—sometimes hours, sometimes days. Colleagues now ask in plain English via Teams, in seconds.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Manual workflows slowed fight insight to a crawl. AI now delivers storylines on demand, freeing analysts to narrate, not wrangle.
Charting bled into evenings and personal time. When Summa required AI at every encounter, adoption jumped from 44% to 86% in four months.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
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.
Real-time fraud and identity verification that combines scoring models with AI reasoning over transaction context, customer history, and unstructured signals.