Acquisitions left data scattered and untrustworthy. A governed Azure platform fixed that—and a new scoring model lifted conversion.
A full-service home remodeling company serving over 300,000 homeowners across 40 states, operating through a national brand and several regional brands acquired over six years of growth.
Multiple acquisitions left the company with fragmented CRM systems, inconsistent data models, and technical debt that made it impossible to trust...
“The risk wasn’t just technical debt. It was our ability to trust our own data enough to make AI decisions on top of it and to stay compliant as we scaled across new geographies.”
Home remodeling services for bathrooms, windows, doors, kitchens, and closets.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Renuity's Lead scoring is part of this use case:
Related implementations across industries and use cases
Reps lost hours manually assessing leads across disconnected systems. Now, AI agents evaluate intent and instantly route top prospects.
Acquisitions scattered critical data across 36+ companies. Now, a principal AI agent routes employee queries to specialized sub-agents.
Staff missed the 30% of leads arriving after hours. AI agents now field 24/7 chats and book tours, prioritizing ready buyers.
Reps lost hours manually assessing leads across disconnected systems. Now, AI agents evaluate intent and instantly route top prospects.
Single-source data gaps stalled global staffing. AI agents now aggregate 10+ providers to map the market in 2 weeks, not 2 months.
FSurging delinquency burdened staff. Now, an AI assistant handles routine collections, freeing teams for high-touch, in-person engagement.
Managers repeated mistakes no one could see. Now calls are scored; stumbles route them to an AI simulator before their next lead.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Acquisitions left data scattered and untrustworthy. A governed Azure platform fixed that—and a new scoring model lifted conversion.
A full-service home remodeling company serving over 300,000 homeowners across 40 states, operating through a national brand and several regional brands acquired over six years of growth.
Multiple acquisitions left the company with fragmented CRM systems, inconsistent data models, and technical debt that made it impossible to trust...
“The risk wasn’t just technical debt. It was our ability to trust our own data enough to make AI decisions on top of it and to stay compliant as we scaled across new geographies.”
Home remodeling services for bathrooms, windows, doors, kitchens, and closets.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Renuity's Lead scoring is part of this use case:
Related implementations across industries and use cases
Reps lost hours manually assessing leads across disconnected systems. Now, AI agents evaluate intent and instantly route top prospects.
Acquisitions scattered critical data across 36+ companies. Now, a principal AI agent routes employee queries to specialized sub-agents.
Staff missed the 30% of leads arriving after hours. AI agents now field 24/7 chats and book tours, prioritizing ready buyers.
Reps lost hours manually assessing leads across disconnected systems. Now, AI agents evaluate intent and instantly route top prospects.
Single-source data gaps stalled global staffing. AI agents now aggregate 10+ providers to map the market in 2 weeks, not 2 months.
FSurging delinquency burdened staff. Now, an AI assistant handles routine collections, freeing teams for high-touch, in-person engagement.
Managers repeated mistakes no one could see. Now calls are scored; stumbles route them to an AI simulator before their next lead.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.