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%
Campaign performance scattered across a dozen tools, stitched together by hand. MINE collapsed it into a single trusted conversation.
HR queries wound through a patchwork of disconnected systems and a separate help desk. One Copilot agent replaced the whole stack.
5+ hours daily on execution left the VP no time for strategy—or a content program at all. AI workflows run the pipeline; campaigns in days.
Every hiring target was a guess. A chain of AI prompts built the first real capacity model in an hour — surfacing a 63% offer-to-start rate.
336 hours a month combing 8M daily logs by hand, always one step behind. Now engineers ask in plain language and act in minutes, not hours.
Teams searched blind across disconnected systems for days. An AI agent now runs the investigation, surfacing root cause automatically.
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.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
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.
Campaign performance scattered across a dozen tools, stitched together by hand. MINE collapsed it into a single trusted conversation.
HR queries wound through a patchwork of disconnected systems and a separate help desk. One Copilot agent replaced the whole stack.
5+ hours daily on execution left the VP no time for strategy—or a content program at all. AI workflows run the pipeline; campaigns in days.
Every hiring target was a guess. A chain of AI prompts built the first real capacity model in an hour — surfacing a 63% offer-to-start rate.
336 hours a month combing 8M daily logs by hand, always one step behind. Now engineers ask in plain language and act in minutes, not hours.
Teams searched blind across disconnected systems for days. An AI agent now runs the investigation, surfacing root cause automatically.
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.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
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.