Legacy HR systems delayed insights by weeks as post-offer dropouts hit 30%. Now, an ML model flags at-risk candidates daily.
A global technology consulting firm supporting over 80,000 users manages complex recruitment pipelines in a fiercely competitive tech talent market.
Post-offer dropout rates jumped as high as 30%, but legacy on-premises HR systems caused weeks-long delays in candidate onboarding predictions....
“The hiring landscape is evolving rapidly, and technology is no longer just an enabler — it is a strategic partner in shaping better candidate experiences and driving measurable outcomes.”
Technology consulting and digital solutions provider for global enterprises.
Cloud-based data warehousing, processing, and analytics platform.
LTIMindtree's Hiring prediction is part of this use case:
Related implementations across industries and use cases
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Reconciling siloed systems delayed revenue forecasts. Now, teams query AI agents in plain language to pull project data in seconds.
Consultants manually sifted through code. GenAI now scans 280 million lines to generate tailored implementation instructions.
Three disconnected systems: one consultant could be recommended for four deals undetected. Now conflicts surface before deals close.
Spreadsheets slowed staffing to eight weeks. AI agents now screen talent and automate contracts, cutting cycle times by 50%.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Phone scheduling took 3 days per candidate. An SMS agent now handles it end-to-end in 37 minutes, 24/7.
Manual reviews bottlenecked 2-hour client deadlines. Now, an AI agent instantly screens applicants to deliver prioritized shortlists.
Legacy HR systems delayed insights by weeks as post-offer dropouts hit 30%. Now, an ML model flags at-risk candidates daily.
A global technology consulting firm supporting over 80,000 users manages complex recruitment pipelines in a fiercely competitive tech talent market.
Post-offer dropout rates jumped as high as 30%, but legacy on-premises HR systems caused weeks-long delays in candidate onboarding predictions....
“The hiring landscape is evolving rapidly, and technology is no longer just an enabler — it is a strategic partner in shaping better candidate experiences and driving measurable outcomes.”
Technology consulting and digital solutions provider for global enterprises.
Cloud-based data warehousing, processing, and analytics platform.
LTIMindtree's Hiring prediction is part of this use case:
Related implementations across industries and use cases
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Reconciling siloed systems delayed revenue forecasts. Now, teams query AI agents in plain language to pull project data in seconds.
Consultants manually sifted through code. GenAI now scans 280 million lines to generate tailored implementation instructions.
Three disconnected systems: one consultant could be recommended for four deals undetected. Now conflicts surface before deals close.
Spreadsheets slowed staffing to eight weeks. AI agents now screen talent and automate contracts, cutting cycle times by 50%.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Phone scheduling took 3 days per candidate. An SMS agent now handles it end-to-end in 37 minutes, 24/7.
Manual reviews bottlenecked 2-hour client deadlines. Now, an AI agent instantly screens applicants to deliver prioritized shortlists.