We help CEOs use AI to make their business more valuable. Here's what happens when companies get full visibility and control over their AI workforce — with governance, cost tracking, and real-time ROI.
Every successful engagement follows this data integration journey:
Customers, pipeline, engagement — the foundation
Workflows, costs, efficiency — the hidden layer
Revenue, profitability, attribution — the bottom line
ClaimAngel had years of customer data in their CRM and claims data in their operations system — but the two never talked. Their CRM's built-in AI could score leads, but it couldn't answer the questions that mattered: Which policyholders were actually profitable? Where was fraud hiding? What was the real cost to underwrite each segment?
Luther started with what the CRM could see: customer engagement patterns, policy types, acquisition channels, and renewal behavior. The CRM's AI flagged "high-value" customers based on premium size — but that turned out to be dangerously incomplete.
Connecting CRM data to claims operations revealed the real story:
Adding financial data completed the picture: true profitability by customer segment, cost-per-claim by channel, and revenue attribution that accounted for operational overhead. The top 20% of customers by premium were only the top 35% by actual profitability.
"Our CRM told us who our biggest customers were. Luther told us who our most profitable customers were. That's a very different list — and it changed everything about how we underwrite."
PaveAmerica tracked deals in their CRM and tracked job costs in their operations system — but nobody connected the two. They were winning bids without knowing which ones would actually be profitable. Their CRM said "great quarter." Their P&L said otherwise.
Starting with CRM pipeline data revealed win/loss patterns the team couldn't see: specific deal characteristics (lot size, customer type, geography) predicted win probability with 85%+ accuracy. But winning wasn't the real problem — winning profitably was.
Cross-referencing CRM deals with operational job costs exposed the gaps:
Adding financial data — actual revenue collected, material costs, labor allocation — turned the picture from "won deals" into "profitable deals." Some of their biggest wins were their worst performers. The data made it obvious; without connecting the systems, it was invisible.
"We thought we knew our business. Then Luther connected our CRM to our job costs and showed us where 30% of our margin was disappearing. We'd never have found that in Salesforce alone."
We don't just sell custom AI agents — we run our entire business on them. 16 AI agents across 4 business units. Before we built our agent platform, our agents were scattered across tools with no governance, no cost visibility, and no way to measure ROI.
We deployed custom AI agents across our own organization — every agent has OKRs, a budget, skills, and a performance scorecard. The platform provides full governance, cost tracking, and ROI visibility across all 16 agents.
This internal work directly led to building Data2Dollars.ai — our AI sales assistant product. When you see the same patterns across 100+ client engagements, you build a product to solve them at scale.
"I built custom AI agents because I needed them myself. 16 agents, 4 business units, and no way to see what they were costing me or delivering. Now every agent has a scorecard, a budget, and accountability. I deploy the same system for my clients."
See how custom AI agents give you governance, cost control, and full visibility over your AI workforce. Book a 30-minute demo — we'll map it to your current setup.