Generative UI Global Hackathon: Agentic Interfaces

Team

UI 3x

Project Concept

Project Description: Adaptive Landing Pages

We are building AI-powered adaptive landing pages that personalize the website experience based on each visitor’s profile, intent, source, and previous behavior.

Instead of showing the same static page to everyone, the landing can adapt its hero, demo, onboarding flow, CTA, pricing message, and interactive modules depending on who is visiting.

How the Page Adapts

The page can adapt using signals such as:

UTM parameters from ads or campaigns.
Referral source such as LinkedIn, Google, email, or partner sites.
Cookies and returning visitor history.
Company/IP enrichment to infer industry, company size, or account type.
CRM or lead data when the visitor is already known.
Micro-inputs such as role selection or one-click onboarding questions.

These signals are converted into a visitor profile, and then an AI agent or rules engine generates the most relevant page structure and content.

First Case Study: Flowly

Our first use case applies this concept to Flowly, a Martech platform that centralizes marketing data, generates insights, and automates decisions.

For Flowly, the landing adapts by role:

CEO: business impact, growth, and strategic decisions.
CTO: APIs, integrations, data pipelines, and security.
CMO: campaign performance, attribution, and creative insights.
CFO: ROI, CAC, LTV, payback, and margin impact.

The adaptive experience may include personalized demos, ROI calculators, campaign simulators, architecture maps, onboarding flows, and role-specific CTAs.

Broader Vision

Although the first case study is Flowly, the system can expand to other industries such as SaaS, fintech, education, healthcare, real estate, and B2B services.

Our goal is to turn static websites into adaptive AI buyer experiences that change based on who is visiting and what they need.

Privacy & Compliance

The experience is designed with privacy with Global and European compliance in mind. Visitors are asked for consent before any personalization is applied. If they do not give permission, they will see the default static landing page instead.

Entry

Status: Submitted

Last saved: May 09 at 5:48 PM -05

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Team Roster

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Samuel Lopez Team Lead RSVP Approved

Co-CEO y CFO at Flowly

Led the product strategy and concept definition for the adaptive landing page system. Samuel structured the core idea, defined the Flowly case study, mapped the different visitor personas, and created the personalized copy for each profile, including CEOs, CTOs, CMOs and CFOs.

I'm a finance undergrad and cofounder @Flowly. Passionate about basketball and good food.

I'm particularly interested in marketing and Martech, and also recently on venture capital.

I'm currently working on Flowly, an automated AI system built to optimize marketing campaigns in paid media allowing agencies to manage clients' accounts more efficiently, save time and acquire more customers while maintaining their team size.

John Botero RSVP Approved

Software engineer at Mercado libre

Focused on building the landing page experience using V0 templates and adapting them into a functional interface. John worked on the visual structure, page components, dynamic sections, and frontend logic needed to render different landing experiences based on visitor profile and intent.

John Botero García is a Software Engineer at Mercado Libre, with over 4 years of experience. He has studied at Platzi, Universidad Nacional de Colombia, and Cesde Institución Educativa.

David cossio RSVP Approved

Student at EAFIT

Focused on the AI and backend logic behind the personalization system. David worked with tools such as Google DeepMind’s A2UI concepts, CopilotKit, and LangChain to connect agent outputs with the frontend, define the generative UI flow, and enable the page to adapt dynamically using visitor context and profile data.

David Cossio is a Student at EAFIT, located in Medellin. He is currently employed and self-employed, with inferred experience of about 3 years. David is an AI Engineer specializing in Machine Learning and Models, with projects like JobPostAI-Final, TravelAgent, and WorkflowGenerator that leverage Python, C++, and TypeScript for AI solutions and automated workflows. He is looking for speaking opportunities and is open to introductions via DM.

Technical architecture, AI-driven applications, distributed systems, machine learning, agentic planning, task automation, end-to-end AI pipelines, automated software engineering tasks.

JobPostAI-Final automates job posting workflows using AI-driven logic. TravelAgent and WorkflowGenerator are AI-oriented applications focused on task automation and agentic planning. Other work includes building distributed data processing systems. These projects leverage a technical stack of Python, C++, and TypeScript to implement practical AI solutions and end-to-end pipelines for automated data handling and software engineering tasks.