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 2,500+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,500+ 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 2,500+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,500+ 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%
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Debugging agents meant replaying sessions by hand. ML updates took a day to validate. Now every step is traceable in real time.
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
A volunteer team couldn't afford a voice actor per language. Now one script ships narrated films in Polish, English, and German at once.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
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.
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.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Debugging agents meant replaying sessions by hand. ML updates took a day to validate. Now every step is traceable in real time.
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
A volunteer team couldn't afford a voice actor per language. Now one script ships narrated films in Polish, English, and German at once.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
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.
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.