AI Success Fee Model

Replace costly, unplanned downtime with AI-driven predictive alerts. We align our fees to the hard cost savings from avoided production halts and extended equipment life.

• Real Example: A heavy equipment manufacturer reduced unplanned downtime by 23% in the first year, translating to over $4.2M in annualized savings on their most critical production line.
• Our success fee was a percentage of the verified savings, ensuring our goals were perfectly aligned with their operational efficiency KPIs.

Automate high-volume, error-prone manual data entry from invoices, contracts, and reports. Our compensation is tied to FTE (Full-Time Equivalent) cost reduction and processing accuracy improvements.

• Real Example: A global insurer automated 85% of its claims intake forms, redeploying 12 FTEs to higher-value work and achieving a 99.1% data accuracy rate.
• The success fee model was based on the audited reduction in manual labor costs, creating immediate, transparent ROI.

Deploy predictive models to identify at-risk customers and trigger personalized retention campaigns. Our fees are contingent on achieving a guaranteed reduction in monthly churn rate.

• Real Example: A SaaS company integrated our churn prediction engine, enabling proactive interventions that reduced their monthly churn by 34% within two quarters, protecting millions in annual recurring revenue (ARR).
• This outcome-based approach directly linked our service to their most critical business metric: customer lifetime value.

Mitigate volatility and reduce costs with AI that optimizes routes, inventory, and carrier selection in real-time. We share the risk and reward, with fees based on percentage savings in logistics spend.

• Real Example: A retailer used our orchestration agent to dynamically reroute shipments during port congestion, cutting average freight costs by 17% and improving on-time delivery by 22%.
• The success fee was calculated from the verified reduction in quarterly logistics expenses, proving value before full payment.

Deflect routine inquiries with AI agents, freeing human agents for complex issues. Our model ties fees to deflection rate targets and escalation cost avoidance.

• Real Example: A telecommunications provider deployed our NLP-powered virtual agent, deflecting 42% of tier-1 support calls and reducing average handle time by 3 minutes per call.
• Compensation was based on achieving the agreed-upon deflection percentage, directly correlating our performance with their operational cost savings.

Enhance security and reduce losses with adaptive models that identify novel fraud patterns. We defer fees until a reduction in fraud-related write-offs is demonstrably achieved.

• Real Example: A payment processor implemented our adaptive detection system, which identified a new fraud vector, leading to a 28% quarter-over-quarter decrease in fraudulent transaction volume.
• The success fee was a share of the recovered revenue, ensuring our solution delivered tangible financial protection.