Platform Architecture &
Infrastructure Overview
For enterprise buyers, technical evaluators, and security teams. A complete view of Propeter's system design, AI pipeline, and infrastructure.
Architecture Philosophy
Propeter is built as a cloud-native, microservices-based platform with 15+ independently deployable services. The architecture is designed for three non-negotiable properties: reliability (the rate engine must never go down during a demand spike), accuracy (AI recommendations must reflect real-time market conditions), and isolation (no tenant's data or workload can affect another).
15+ independent services. Each can be deployed, scaled, and updated without affecting others. Failure in one service is contained.
Non-critical paths use async messaging (SQS). Critical paths (rate push, PMS sync) are synchronous with circuit breakers.
Strict separation of config from code. Stateless application processes. No filesystem state. All config via environment variables and AWS Secrets Manager.
The 6-Agent AI Architecture
Propeter's core intelligence is delivered by a pipeline of six specialised AI agents. Each agent has a specific responsibility, data input, and output. The pipeline runs on a 4-hour cycle for standard updates, with Market Intelligence running independently every 15 minutes.
Infrastructure Architecture Diagram
Seven layers from client to disaster recovery. Colour-coded by function.
Propeter Platform — Infrastructure Overview
End-to-End Data Flow
How a rate recommendation is generated — from raw PMS data to a live rate on Booking.com.
PMS Data Pull (Every 4 hours)
Data Ingestion Agent connects to the hotel's PMS via REST API or webhook. Pulls new reservations, cancellations, modifications, and availability updates. Normalises data to Propeter's internal schema.
Market Intelligence Refresh (Every 15 min)
Market Intelligence Agent independently scrapes competitor rates from configured OTA URLs. Updates the competitive rate index in Redis. Triggers parity alerts if violations detected.
Demand Forecast Calculation
Demand Forecast Agent runs XGBoost + LSTM ensemble against 365-day forward window. Combines 200+ signals: historical pace, events calendar, flight search trends, weather, competitor availability. Outputs occupancy probability per date.
Price Elasticity Calculation
Price Elasticity Agent models the booking probability curve at different price points for each room type on each date. Updated continuously with incoming booking events via Bayesian updating.
Revenue Optimisation
RevPAR Optimisation Agent solves for the rate that maximises expected revenue: f(rate) = booking_probability(rate) × rate × remaining_nights × rooms. Produces a candidate rate per room type per date.
Strategy Agent — Guardrails & Business Rules
Candidate rate is passed through the Strategy Agent which applies: floor/ceiling guardrails, min-stay restrictions, close-to-arrival rules, promotional overrides, and any manual rules the Revenue Manager has set. Produces the final recommendation.
Rate Engine — 13-Stage Processing
Final recommendation enters the 13-stage Rate Engine: Base Rate → Inventory Adjustment → Rate Plan → Derived Rates → Promotion → Loyalty → Voucher → Referral → Flash Deal → Stacking Resolver → Guardrails → Upsell → Tax & Fee. Output: distribution-ready rate object.
Channel Distribution
Rate object pushed to channel manager via REST API. Channel manager distributes to all connected OTAs (Booking.com, Expedia, MakeMyTrip, etc.). Rate confirmation received and logged. Parity check runs automatically.
Integration Architecture
Propeter integrates with the hotel's existing technology stack — not replacing it, but intelligently orchestrating it.
🏨 PMS REST + Webhooks
OAuth 2.0 auth. Bidirectional sync. Supported: Opera Cloud, Mews, Cloudbeds, Clock, eZee, IDS Next, Hotelogix. Custom: REST API for others.
📡 Channel Manager HTNG / REST
SiteMinder, RateGain, Staah, Cloudbeds CM. Rate push + availability sync. <60 second push latency.
💳 Payments Tokenisation API
Windcave/Qvalent PCI DSS Level 1. Card numbers never touch Propeter systems. Token reference only.
📊 Accounting Xero REST API
OAuth 2.0. Real-time revenue sync. Auto-categorisation by room type, channel, rate plan. Nightly reconciliation.
📧 Email / SMS REST API
SendGrid (transactional email), Twilio (SMS). Guest communication sequences. Pre-arrival, upsell, post-stay.
📱 Push Notifications FCM / APNs
Google Firebase Cloud Messaging (Android), Apple Push Notification Service (iOS). Staff alerts and guest app notifications.
📈 Marketing CRM REST + Webhooks
mkng360.com integration. 360° guest profiles, campaign attribution, email marketing automation.
🔐 Auth / SSO OAuth 2.0 / SAML
Native login + SSO for Enterprise (SAML 2.0). Google Workspace SSO for staff. MFA enforced.
📋 Competitor Intel Proprietary Scraping
Lighthouse data + proprietary OTA monitoring. 200+ demand signals. 15-minute competitive rate refresh.
Propeter's REST API is available to Professional and Enterprise clients. Base URL: api.propeter.com/v2. Authentication: OAuth 2.0 bearer tokens (24h expiry + refresh rotation). Rate limits: 1,000 req/hour (Professional), 10,000 req/hour (Enterprise). Full documentation available in your account portal.
Performance & Scalability
| Component | SLA / Target | Current Performance | Scaling Model |
|---|---|---|---|
| API Response Time | <500ms p95 | <180ms p95 | ECS auto-scale on CPU 70% |
| Rate Engine Processing | <200ms p99 | <85ms p99 | Horizontal pod scaling |
| Rate Push to Channel | <60 seconds | <12 seconds avg | Async queue processing |
| OTA Rate Live | <15 minutes | 8–12 minutes avg | Channel manager dependent |
| Dashboard Load (CDN) | <1.5s FCP | <0.8s FCP (CloudFront) | CloudFront edge caching |
| AI Forecast Cycle | <4 hours | 2.5–3 hours per property | Parallel per-property workers |
| Platform Uptime | 99.9% monthly | 99.94% (last 12 months) | Multi-AZ, auto-failover |
| Max Concurrent Properties | 10,000+ | Designed for 50,000 | Tenant-partitioned workers |
Multi-Tenancy & Data Isolation
Propeter is a multi-tenant platform where hundreds of hotel properties share the same application infrastructure. Strict isolation ensures that no tenant's data, workload, or failure can affect another.
Isolation Mechanisms
Database Layer
Row-Level Security (RLS) in PostgreSQL — tenant_id column on every table. Every query automatically filtered by tenant context. Cross-tenant queries are impossible at the database layer regardless of application bugs.
Application Layer
Tenant context loaded from JWT at request time. All service-to-service calls include tenant context header. No shared in-memory state between tenant requests. Separate Redis key namespacing per tenant.
AI Model Isolation
Each property has its own AI model instance. Model training data never crosses tenant boundaries. A property's demand patterns, pricing history, and guest data are exclusively used for that property's recommendations.
Resource Quotas
Per-tenant rate limits on API endpoints. AI compute quotas prevent one large property from monopolising forecast resources. Queue depth limits per tenant prevent noisy-neighbour effects.
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