Hosted Seats - Reinventing Hospitality Operations Using Generative AI on AWS
About the Customer
Hosted Seats delivers premium hospitality and guest lifecycle management solutions for some of the world’s most prestigious global events, including the Olympics, World Cup and Coachella Valley Music and Arts Festival.
Working with global brands such as Coca-Cola, Hosted Seats powers curated hospitality packages that combine event tickets, hotel accommodations, transportation and exclusive VIP experiences. The platform operates at global scale, supporting high guest volumes, complex logistics coordination and event execution.
Hosted Seats is built as an AWS native SaaS platform designed to handle high traffic workloads, white label customization for enterprise partners and automated logistics workflows. The system manages the complete end to end guest lifecycle, including registration, invitation tracking, itinerary changes, hotel assignments, fulfillment operations, order approvals and billing. Given the scale and operational complexity of global events, the platform requires real time data visibility, quick decision making and scalable analytics capabilities to deliver seamless and personalized guest experiences.
Challenge
As Hosted Seats scaled, analytics and reporting became a bottleneck for operational efficiency.
Key Challenges
1. Heavy Dependency on Manual SQL & Analysts
Business teams relied on manual SQL queries, spreadsheet-based reporting and static dashboards. Ad hoc analysis required analysts to build custom queries, often taking days to deliver insights. This slowed critical decisions related to ticket allocation, hotel block management, guest segmentation and upsell opportunities.
2. Complex Dashboards & Data Silos
Operational insights were distributed across multiple dashboards (Guest Profile, Invitation, Fulfillment, Itinerary, Orders, Billing). Extracting a unified view required manual filtering, cross-referencing timestamps and navigating complex joins across relational datasets hosted in Amazon RDS.
3. Slow Decision Making During Events
Identifying daily arrivals, incomplete registrations, pending order approvals, or hotel capacity utilization required repetitive manual filtering and recalculations. During peak operations, this approach was inefficient, error prone and not scalable.
4. Limited Personalization & Operational Agility
Manual workflows restricted the ability to:
- Quickly segment guests
- Match preferences with hotel inventory
- Identify approval bottlenecks
- Track guest lifecycle changes in real time
This led to slower response times, higher operational overhead and reduced ability to deliver personalized guest experiences at scale.
Solution
To solve the reporting and operational challenges, Tech Holding helped HostedSeats using AWS native services, The core objective was to reduce dependency on manual SQL queries and make operational data easily accessible to business users. The solution was built using Amazon Quick Suite as the analytics layer, directly connected to Amazon RDS where all guest lifecycle, order, hotel, and billing data is stored.
Amazon Quick Suite was configured with optimized datasets built from relational tables in Amazon RDS. Existing dashboards were streamlined for key operational metrics such as arrivals, departures, order status, hotel allocations, and guest activity tracking. On top of this, Amazon Quick Suite to provide natural language querying capabilities. This allowed users to ask questions in plain English and receive instant visual results without needing to understand database schema, joins, or filter logic.
With the latest Generative BI capabilities in Amazon Quick Suite, the platform now automatically interprets user intent, generates the required query logic, applies correct filters, and produces both visualizations and short contextual summaries. The architecture remains simple and scalable. Data continues to reside in Amazon RDS, QuickSight handles the analytics and visualization layer, and QuickSuite enables conversational access to data. This ensured minimal architectural disruption while significantly improving usability and speed of insights.
The solution leverages QuickSuite as a fully managed service, which is designed for high availability by default. Supporting services such as RDS, ECS (Fargate), and ALB are deployed across multiple Availability Zones to ensure resilience. The architecture is designed to achieve an RTO of under and an RPO of under 5 minutes, with ECS services automatically recovering across AZs and databases using Multi-AZ replication to minimize data loss and downtime.
AWS account governance was implemented using standard security practices, including restricted root account usage limited to administrative scenarios with no programmatic access and mandatory MFA enabled for root and all privileged users. Account contact details are configured using corporate distribution email IDs to ensure shared visibility of billing and security notifications. AWS CloudTrail is enabled across all regions and integrated with centralized logging, with logs stored in a secured S3 bucket with restricted access and versioning, ensuring auditability, monitoring, and compliance with governance requirements.
TechHolding implemented a centralized access management approach using AWS Identity Center, enabling secure and scalable access across environments. Users authenticate using individual credentials integrated with the organization’s identity system, supporting Single Sign-On for AWS Console access. Programmatic and CLI access is enabled using temporary credentials through IAM role assumption (AWS STS), eliminating the use of static access keys. Access permissions are structured using role-based controls with least privilege enforcement, ensuring secure, auditable, and compliant access for both users and services.
Key Use Cases and Operational Impact
Generate a Daily Arrival and Departure Summary per Hotel
Operations teams can request a daily or date range summary and instantly receive hotel wise arrival and departure counts. The system automatically validates the date input, applies conditional logic to separate arrivals and departures, and aggregates results by hotel property. This replaces a repetitive manual filtering process that previously required deep dataset knowledge and analyst involvement.
It is implemented as a staged workflow with built in true and false validation logic. The flow behaves like a lightweight custom analytics agent that ensures only confirmed and operationally relevant bookings are included. If no valid date is provided, it applies default operational logic. If no data exists, it returns structured zero results instead of blank outputs.
It reduces reporting errors, standardizes daily hotel movement summaries, and enables operations teams to proactively manage room readiness, transport coordination, and staffing allocations without relying on analysts.
Guests with Incomplete Registrations
Earlier, identifying incomplete guest registrations required manual filtering across multiple fields such as passport details, contact information, and profile status. Users needed to understand which fields were mandatory and apply multiple null or empty value filters. This process was repetitive and often delayed follow ups, leading to last minute issues during check in.
With Generative BI enabled in Amazon Quick Sight, users can simply ask which guests have not completed their registration. The system automatically evaluates mandatory fields, checks for missing or partial data, and generates an updated list instantly. This allows operations teams to proactively follow up with guests, reduce check in risks, and improve overall event readiness.
Order Approval Tracking
Previously, tracking order approvals required switching between multiple dashboards such as order status, approval workflows, and billing views. Users had to manually apply filters to identify pending approvals and understand how many guests were impacted. This slowed decision making and created bottlenecks during peak booking periods.
With Generative BI, business users can instantly see how many orders are pending, approved, or rejected by simply asking a question. The system automatically aggregates approval status, links related guest counts, and presents a clear summary. This improved visibility into approval bottlenecks, accelerated escalations, and reduced delays in guest confirmations.
Program Level Capacity Monitoring
Monitoring guest allocations across programs and hotels was previously a manual task involving dataset joins, grouping logic, and separate filtering for each program. Sales and coordination teams depended on analysts to calculate guest counts, capacity utilization, and remaining availability. This made real time optimization difficult during high demand events.
With the new implementation, users can request program wise guest counts and hotel allocations in seconds. The system automatically groups data by program and property, calculates totals, and presents an accurate overview. This supports faster ticket allocation decisions, better capacity planning, and optimized hotel block usage across global events.
Unified Guest Activity Tracking
Guest activity data was previously spread across separate dashboards for invitations, profile updates, fulfillment, and itinerary changes. To understand what happened for a specific guest, users had to manually switch between views, apply guest level filters, and compare timestamps. This was time consuming and increased the risk of missing important updates.
With Generative BI, users can request recent activity for a guest and receive a consolidated, chronological summary. The system automatically combines data from multiple activity sources and presents a unified narrative. This improves auditability, speeds up issue resolution, and significantly reduces manual correlation effort for operations teams.
Follow Up Conversational Analysis
Traditional dashboards required users to redesign filters or create new views for every additional question. After reviewing one insight, any follow up query meant repeating the entire filtering process. This created dependency on analysts for ad hoc analysis and slowed operational workflows.
With conversational analytics enabled, users can naturally ask follow up questions within the same context. After reviewing daily arrivals, they can immediately ask about late check ins or special requirements without rebuilding reports. This makes data exploration more intuitive, reduces analyst workload, and allows faster operational decisions during high volume events.
Overall, this implementation improved self service analytics for non technical users while reducing workload on the analyst team. Operational decisions that previously took hours or days are now completed in seconds. The solution also scales smoothly during high traffic global events since the analytics layer is fully managed within AWS.
Results and Benefits
The implementation delivered measurable business and financial impact.
1. Reduction in Manual Reporting Effort
Time spent on manual SQL queries, dashboard filtering, and spreadsheet reporting was reduced by approximately 65%. Operational teams now retrieve answers in seconds instead of hours.
2. Improvement in Operational Efficiency
Sales, hospitality, and event coordination teams achieved nearly 40% efficiency gains by eliminating repetitive manual analysis and reducing dependency on analysts.
3. Democratized Data Access
Non-technical users can now self-serve insights without:
- Understanding database schemas
- Knowing field names
- Building calculated metrics
- Switching across dashboards
This significantly reduced the workload on analytics and engineering teams.
4. Faster Data Driven Decisions
Critical decisions regarding:
- Ticket allocations
- Hotel block optimisation
- Order approvals
- Guest segmentation
are now made in near real time, enabling proactive operational adjustments during high-volume events.
5. Reduced Human Error
Automated query generation and contextual summarization reduced the risk of incorrect filters, partial views, or inconsistent interpretations across teams.
6. Improved Guest Personalisation at Scale
By instantly surfacing:
- Special requirements
- Registration completeness
- Itinerary changes
- Order approval status
HostedSeats enhanced its ability to deliver personalized guest experiences efficiently, even during peak demand.
AWS Services Used
- Amazon QuickSuite
- Amazon ECS with Fargate
- Amazon EC2
- AWS ALB
- Amazon VPC
- Amazon Aurora - PostgreSQL
- AWS DocumentDB
- Amazon Route53
- Amazon S3
- Amazon CloudFront
- AWS Parameter store
- Amazon SNS
- Amazon SQS
- Amazon SES
- Amazon EventBridge
- Amazon CloudWatch
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