Zero-Cost AI Pilot Until ROI

Automate the extraction and classification of data from invoices, contracts, or application forms, eliminating manual data entry.

• Real Example: A financial services provider processed 50,000 loan documents monthly, achieving 99% accuracy and reducing processing time from 10 minutes to 30 seconds per document.
• The ROI Fix: The pilot is funded by the labor cost savings it generates. Fees are triggered only after processing costs fall below a predefined threshold.

Use an AI control tower to optimize inventory levels, shipping routes, and supplier allocations in real-time based on demand signals and disruption alerts.

• Real Example: A retailer reduced excess inventory by 18% and improved on-time in-full (OTIF) delivery by 12% during a volatile quarter.
• The ROI Fix: The pilot's success is measured by reduced carrying costs and improved service levels. Our compensation is a share of the hard-dollar savings verified in your P&L.

Deploy models that analyze historical CRM data and external signals to prioritize sales leads most likely to convert, increasing sales team productivity.

• Real Example: A B2B software company increased its sales-qualified lead conversion rate by 28% and reduced lead follow-up time by 40%.
• The ROI Fix: We align our fees to the incremental revenue generated from the uplift in lead conversion, providing a clear, attributable return on the AI investment.

Implement machine learning models to analyze transaction patterns in real-time, identifying fraudulent activity with higher accuracy than rule-based systems.

• Real Example: A fintech platform reduced false positives by 60% and caught 15% more sophisticated fraud attempts during the pilot, directly protecting revenue.
• The ROI Fix: Our pilot costs are offset by the value of fraud prevented. We share in the savings from reduced fraud losses and operational review costs only after they are realized.

An automotive parts supplier experienced unplanned downtime costing $500k per incident. Our pilot deployed an Edge AI sensor network with real-time inference to predict equipment failures.

• Outcome: The system provided 14-day advance warnings for critical failures, reducing unplanned downtime by 42% in the first four months.
• This translated to $850k in avoided losses from production halts and maintenance overruns, creating a clear ROI threshold before the engagement converted to a full contract.

A SaaS company with a 12% annual churn rate needed to protect its recurring revenue stream. We launched a predictive analytics pilot using behavioral data to identify at-risk customers and trigger personalized retention workflows.

• Outcome: The pilot identified 90% of likely churn events 30 days in advance, enabling proactive saves that reduced churn by 3.2 percentage points.
• This retained $1.2M in annual recurring revenue (ARR) within the pilot window, creating undeniable financial justification for scaling the solution.

A national retailer struggled with generic marketing, achieving only a 1.5% email conversion rate. Our Zero-Cost Pilot implemented an agentic commerce AI that built individual customer models to personalize product recommendations and promotions in real-time.

• Outcome: Conversion rates increased to 4.7% and average order value rose by 18% during the pilot period.
• The incremental revenue directly attributed to the AI exceeded $300k, crossing the ROI threshold and triggering the performance-based fee structure.

A financial services firm dedicated a team of 12 analysts to manual monthly reporting, prone to errors under tight deadlines. We piloted a neuro-symbolic AI system that automated data aggregation, validation, and draft generation for regulatory filings.

• Outcome: The AI reduced report generation time by 70% and eliminated 100% of manual data entry errors.
• This saved over 800 person-hours per month, allowing the team to shift to strategic analysis. The quantifiable labor savings formed the basis for the ROI calculation that initiated our fees.