Rental Management System

A booking system for vehicle and property rentals: no double bookings, prices that adjust to demand, and an AI support assistant. Booking conversion rose 38%.

Vehicle and property rentals with prices that follow demand and an AI assistant that answers bookings 24/7.

Fleet and property managers adjusted prices by hand ahead of holidays and peak weekends, usually too late. Customer questions about bookings waited for staff to reply.

The business needed pricing that followed demand and support that answered at any hour, without double-booking an asset.

Fleet and asset rental managers struggled with manual pricing adjustments during peak holiday demand and slow customer service responses.

• •Prevent double bookings when several customers reserve the same asset at once.
• •Price each booking from forecast demand within safe limits.
• •Answer routine booking questions automatically, accurately, and around the clock.

I built the Laravel booking engine and the Angular booking flow, integrated the Python demand-forecasting service, and designed the LLM support agent and its guardrails.

Built full-stack Laravel/Angular booking engine integrated with Python predictive analytics for demand-based pricing models and Gemini/ChatGPT APIs for 24/7 automated conversational support.

1. 01

   Angular booking flow

   Search, availability calendar, quote and checkout. Quotes come from the API, never computed in the browser.

2. 02

   Laravel booking engine

   Owns assets, calendars and reservations. Reservations take a lock on the asset’s dates so simultaneous attempts cannot both succeed.

3. 03

   Python analytics service

   Forecasts a demand index per asset and date from historical occupancy and seasonality. Laravel turns that into a surge multiplier with a hard cap.

4. 04

   LLM support agent

   Gemini and ChatGPT behind a streaming chat that extracts reservation intent, then calls the booking API for facts instead of answering from the model.

5. 05

   Redis

   Caches availability and quotes, and holds short-lived locks during reservation.

• •Concurrency-safe reservations on shared availability calendars.
• •Demand-based dynamic pricing with a long-stay discount rule.
• •A streaming support assistant that extracts intent and checks real data before replying.

// Dynamic Pricing Calculator & AI Demand Model Integration
async function calculateDynamicRentalRate(assetId: string, startDate: Date, durationDays: number): Promise<RentalQuote> {
  const baseRate = await AssetService.getBaseRate(assetId);
  const occupancyForecast = await PythonAnalyticsClient.predictDemandFactor({ assetId, startDate });
  const surgeMultiplier = Math.min(1.85, Math.max(1.0, occupancyForecast.demandIndex * 1.25));
  
  return {
    dailyRate: baseRate * surgeMultiplier,
    totalEstimated: baseRate * surgeMultiplier * durationDays,
    aiRecommendedDiscount: durationDays >= 7 ? 0.15 : 0.0
  };
}

1. 01

   Concurrency locking on vehicle availability calendars during simultaneous multi-user reservation attempts.

   Short-lived locks on the asset and date range during reservation, with the second attempt receiving a clear conflict and fresh availability.

2. 02

   Calibrating dynamic pricing algorithms against historical occupancy data and seasonal trends.

   Calibrated the pricing model against historical occupancy and seasonal patterns, then bounded its output with a minimum and maximum multiplier.

3. 03

   Designing streaming LLM support agents that accurately extract reservation intent without hallucinations.

   Structured intent extraction followed by API calls for facts, so replies are grounded in the booking system rather than the model’s guess.

Security

Payment and personal data stay in the Laravel backend. The LLM receives only the minimum context needed to answer, and its actions are limited to the booking endpoints it is allowed to call.

Speed & growth

Availability and quotes are served from Redis. Support resolution stays under 30 seconds because the assistant answers routine questions without a human hand-off.

Concurrent reservation tests fire simultaneous bookings at the same asset and assert only one succeeds. Pricing is checked against historical periods, and the assistant is tested with scripted conversations for intent accuracy.