AI dynamic pricing for car rental: RickSalto's pitch and investor feedback

Executive overview

Car rental and mobility companies set prices manually across hundreds of variables — car class, location, rental length, time of day — and the complexity guarantees errors and missed revenue. RickSalto's AI-powered dynamic pricing engine automates this by ingesting historical data, competitor prices, demand signals, and up to 500 data points to push optimised prices directly into fleet management systems and OTAs.

The founding team built and exited one of Europe's largest car-sharing companies, generating €75M annual revenue cash-flow positive, making pricing their core competency.

Operators don't lose revenue because of bad luck — they lose it because manual pricing at scale is impossible.

The product and how it works

• Ingests historical company data, market data, competitor prices, weather, flight info, and booking signals
• Uses multiple ML models that improve with each location's performance over time
• Pushes prices back into fleet management systems or OTAs automatically
• Serves car rental (20–40 data points) and mobility companies (up to 500 data points)
• Pricing model: per car per month (SaaS), not revenue-share

Target customers

• Small operators (50–100 cars) with no proprietary pricing — compete against Avis, Enterprise using third-party tools
• Large enterprise groups managing franchisee networks — need consistent pricing and quality across hundreds of thousands of vehicles
• Mobility platforms (micro-mobility, ride-hail) that use RickSalto via white-label reseller integrations

Why revenue-share pricing failed

• Baseline comparison is impossible: seasonality shifts (Easter, Ramadan moving months) make year-on-year comparisons unreliable
• Customers dispute every uplift, attributing gains to external factors rather than pricing
• One revenue-share customer remains; they are waiting for the contract to lapse
• Per-car SaaS pricing removes the attribution argument and provides predictable revenue

Traction and raise

• 20 paying and piloting customers
• Six reseller integrations
• ~$200K ARR
• Raising $1.5M pre-seed; $950K closed, $550K remaining

Investor feedback

• Lead investor praised the founder's domain credibility from the prior exit
• Recommended showing customer count on slide one — before product explanation — to signal early validation immediately
• Flagged website copy as unclear; the pitch itself communicated the value far better
• Recommended presenting team credentials after the problem slide, not before
• Noted that "AI" was used as a buzzword across all pitches; RickSalto should sharpen its AI narrative to explain why ML is essential here, not just assumed
• Suggested a visible growth roadmap: 6 customers now → 20 in year one → 1,000 in five years