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 →
Leads arrived unqualified, pulling engineers into days of BANT research. Two agents now qualify every inbound and brief sellers in minutes.
Customer context scattered across 50+ systems; AI agents now surface tenure, eligibility, and ranked picks before the expert says hello.
Thousands of emails, every application reviewed by hand. Now agents verify IDs, translate, and draft; a five-person team makes every call.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
Reps put customers on hold to search 100GB of manuals and fragmented records. Arthur surfaces the right answer mid-call — no hold.
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.
Leads arrived unqualified, pulling engineers into days of BANT research. Two agents now qualify every inbound and brief sellers in minutes.
Customer context scattered across 50+ systems; AI agents now surface tenure, eligibility, and ranked picks before the expert says hello.
Thousands of emails, every application reviewed by hand. Now agents verify IDs, translate, and draft; a five-person team makes every call.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
Reps put customers on hold to search 100GB of manuals and fragmented records. Arthur surfaces the right answer mid-call — no hold.
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.