Shayla Khezr

Rebot

An illustrative sketch of a flower

Web based MVP for optimizing pricing & reporting with AI.

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

The problem

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

We designed an AI Revenue web to centralize and automate revenue management:

With Rebot, revenue managers reduced manual benchmarking from hours per hotel to an automated daily process. Instead of checking prices for only 2–3 hotels per day, they can now monitor 100+ hotels simultaneously, with AI scraping generated in <10 seconds. This efficiency unlocks up to 5–10% additional revenue through dynamic pricing.

Heatmap

Traditional heatmaps are common tools for revenue managers, but they’re often overwhelming, packed with dense grids, endless rows of dates, and heavy colors. While they show a lot of data, they make it difficult to scan quickly or spot meaningful patterns.

Heatmap ADR

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August

Low ($100)

Medium ($200)

High ($300)

Scraper

Revenue managers waste hours tracking competitors prices manually, so working with the dev-built scraper helped me understand how this tool could solve this problem for the user and translate it into a clear, actionable table.

An illustrative sketch of a flower

Design Iterations

This example is one of the iterations that shows how the design evolved, starting simple in low-fi, testing actions in the first design, and refining into a clearer final version.

An illustrative sketch of a flower

First Version

In the first high-fidelity design, we introduced AI recommendations alongside the three action buttons (Accept, Ignore, Edit). While this added intelligence, the card became visually saturated.

An illustrative sketch of a flower

Final Design

After testing and reflection,

I simplified the interaction. The final card has a cleaner layout with fewer but clearer options, allowing managers to quickly understand the recommendation and act with confidence.

Shayla Khezr

Rebot

An illustrative sketch of a flower

Web based MVP for optimizing pricing & reporting with AI.

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

The problem

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

We designed an AI Revenue web to centralize and automate revenue management:

With Rebot, revenue managers reduced manual benchmarking from hours per hotel to an automated daily process. Instead of checking prices for only 2–3 hotels per day, they can now monitor 100+ hotels simultaneously, with AI scraping generated in <10 seconds. This efficiency unlocks up to 5–10% additional revenue through dynamic pricing.

Heatmap

Traditional heatmaps are common tools for revenue managers, but they’re often overwhelming, packed with dense grids, endless rows of dates, and heavy colors. While they show a lot of data, they make it difficult to scan quickly or spot meaningful patterns.

Heatmap ADR

Mon

01

Mar

02

Wed

03

Thu

04

Fri

05

Sat

06

Sun

07

Mon

08

Mar

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August

Low ($100)

Medium ($200)

High ($300)

Scraper

Revenue managers waste hours tracking competitors prices manually, so working with the dev-built scraper helped me understand how this tool could solve this problem for the user and translate it into a clear, actionable table.

An illustrative sketch of a flower

Design Iterations

This example is one of the iterations that shows how the design evolved, starting simple in low-fi, testing actions in the first design, and refining into a clearer final version.

Product detail

First Version

In the first high-fidelity design, we introduced AI recommendations alongside the three action buttons (Accept, Ignore, Edit). While this added intelligence, the card became visually saturated.

Product detail

Final Design

After testing and reflection,

I simplified the interaction. The final card has a cleaner layout with fewer but clearer options, allowing managers to quickly understand the recommendation and act with confidence.

Shayla Khezr

Shayla Khezr

Rebot

An illustrative sketch of a flower

Web based MVP for optimizing pricing & reporting with AI.

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

The problem

In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.

The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.

We designed an AI Revenue web to centralize and automate revenue management:

With Rebot, revenue managers reduced manual benchmarking from hours per hotel to an automated daily process. Instead of checking prices for only 2–3 hotels per day, they can now monitor 100+ hotels simultaneously, with AI scraping generated in <10 seconds. This efficiency unlocks up to 5–10% additional revenue through dynamic pricing.

Heatmap

Traditional heatmaps are common tools for revenue managers, but they’re often overwhelming, packed with dense grids, endless rows of dates, and heavy colors. While they show a lot of data, they make it difficult to scan quickly or spot meaningful patterns.

Heatmap ADR

Mon

01

Mar

02

Wed

03

Thu

04

Fri

05

Sat

06

Sun

07

Mon

08

Mar

09

Wed

10

Thu

11

Fri

12

Sat

13

Sun

14

Mon

15

Mar

16

Wed

17

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30

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August

Low ($100)

Medium ($200)

High ($300)

Scraper

Revenue managers waste hours tracking competitors prices manually, so working with the dev-built scraper helped me understand how this tool could solve this problem for the user and translate it into a clear, actionable table.

An illustrative sketch of a flower

The Design Process

I approached this project with a design thinking mindset, starting with real user pain points, validating needs quickly, and iterating fast. Along the way, I used AI as a partner to question assumptions, explore ideas, and speed up smaller decisions.

  • How revenue managers work
  • Current tools, frustrations & workflows

• Problem statement• Business goals

• Fast wireframes to validate ideas • AI-powered flows: scraping, PMS sync, smart pricing • Familiar UI for managers

• Iterated based on real feedback• Reduced visual noise• Clear hierarchy

• Dev team building the MVP• Internal testing in progress• Next step: test with real revenue managers!

Understand

Define

Ideate

Build & Iterate

Implement

Design Iterations

This example is one of the iterations that shows how the design evolved, starting simple in low-fi, testing actions in the first design, and refining into a clearer final version.

Product detail

First Version

In the first high-fidelity design, we introduced AI recommendations alongside the three action buttons (Accept, Ignore, Edit). While this added intelligence, the card became visually saturated.

Product detail

Final Design

In the first high-fidelity design, we introduced AI recommendations alongside the three action buttons (Accept, Ignore, Edit). While this added intelligence, the card became visually saturated.