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 2,400+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,400+ 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 2,400+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,400+ 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%
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Most agents had never worked a contact center. Information scattered, scripts unmemorable. Real-time guidance walked them through each call.
Manual disposition entry drove 20% error rates. AI-suggested codes and auto-summaries cut that to under 1%.
EU data rules locked out public AI; every doc and plan written by hand. Now one copilot searches emails, Slack, and docs—within EU borders.
Marketers stitched campaign data together by hand—across dashboards, docs, and tools. Now they ask MINE and get a sourced answer in seconds.
Acquisitions fragmented data into silos; regulated datasets took months to unlock for AI. Unified governance made them available instantly.
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.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
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.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Most agents had never worked a contact center. Information scattered, scripts unmemorable. Real-time guidance walked them through each call.
Manual disposition entry drove 20% error rates. AI-suggested codes and auto-summaries cut that to under 1%.
EU data rules locked out public AI; every doc and plan written by hand. Now one copilot searches emails, Slack, and docs—within EU borders.
Marketers stitched campaign data together by hand—across dashboards, docs, and tools. Now they ask MINE and get a sourced answer in seconds.
Acquisitions fragmented data into silos; regulated datasets took months to unlock for AI. Unified governance made them available instantly.
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.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
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.