Case Study - WhatsApp Flood Preparedness Agent

Integrations

Overview

Atram AI is addressing one of the most pressing challenges of climate change: helping vulnerable communities adapt to increasingly unpredictable weather patterns. In Kenya, small business owners in flood-prone areas face devastating economic losses when floods damage their inventory, disrupt operations, and force emergency closures.

Despite having experiential knowledge about flood risks, these entrepreneurs lacked access to timely, accurate weather information and struggled to implement effective preparedness strategies during the stress of daily business operations. Many relied on informal weather reports or waited until floods occurred before taking action, resulting in expensive emergency measures and significant losses.

The challenge was to create an accessible AI solution that would:

• Provide accurate, location-specific weather forecasts
• Deliver actionable flood preparedness advice tailored to small businesses
• Enable proactive planning rather than reactive crisis management
• Work through familiar technology that requires no technical expertise
• Build trust with users who had limited experience with AI tools

Solution

We analyzed the requirements and designed a comprehensive solution that went beyond simple weather reporting to become a personal flood advisor for business owners.

Agentic AI Architecture

The core innovation was implementing agentic capabilities that enable intelligent, context-aware conversations. Rather than following rigid scripts, the Atram Agent can:

Understand Intent and Context: The agent analyzes user questions to determine their specific needs, whether seeking weather forecasts, flood advice, or alert subscriptions.

Route Conversations Intelligently: Based on the query, the agent seamlessly directs users to the appropriate workflow—weather forecasts, knowledge base queries, or alert subscription flows.

Handle Ambiguity: When questions lack context, the agent engages in natural back-and-forth dialogue to clarify user needs before providing answers.

Adapt Responses: The agent tailors its communication style and level of detail based on user preferences and context, from concise summaries to detailed explanations.

Technical Implementation

Voiceflow Development Platform: The chatbot is built entirely in Voiceflow, leveraging its visual workflow system, AI agent capabilities, and built-in knowledge base. We created modular, reusable components that make the system maintainable and scalable.

Supabase Database: A flexible PostgreSQL database stores user profiles with phone numbers, location data (coordinates and names), notification preferences, and alert subscriptions. The schema accommodates multiple use cases and future expansion.

Weather Forecast API: We developed a custom API hosted on Vercel that serves as a proxy to Open-Meteo, fetching location-specific weather data and translating raw meteorological information into human-readable formats optimized for business decision-making.

Knowledge Base Management: The flood preparedness content is managed in Airtable and synced to Voiceflow's vector database. This enables semantic search queries that match user questions to relevant preparedness tactics and FAQs based on meaning rather than exact keywords.

WhatsApp Integration: Deployed through FlowBridge, the WhatsApp interface proved critical for accessibility. Users interact with sophisticated AI technology through the familiar WhatsApp interface they use daily, eliminating barriers to adoption.

Conversation Design

We carefully crafted the conversational experience to serve users in high-stress situations:

Personality: Friendly, patient, and trustworthy—projecting professional competence while maintaining approachability. The agent maintains a helpful attitude even when users express frustration or uncertainty.

Decision Support: Rather than overwhelming users with data, the agent provides clear, actionable conclusions that support decision-making: "Should I close my shop?" "When should I move inventory?"

Progressive Disclosure: Information is delivered in digestible chunks. Users can request more detail when needed, but initial responses focus on immediate actionable insights.

Practical Examples: Abstract advice is reinforced with concrete implementation guidance tailored to small business contexts.

Key Features Delivered

Weather Forecasts: Users can request forecasts for their registered location or specify different locations. The agent provides both immediate forecasts and multi-day planning information, translating complex meteorological data into business-relevant insights.

Rain Alert Subscriptions: Users share their location using WhatsApp's built-in location feature. The system stores coordinates and enables proactive alert notifications through FlowBridge when severe weather threatens their area.

Flood Preparedness Knowledge Base: Users can ask questions about protecting inventory, electrical safety during floods, emergency planning, and business continuity. The knowledge base serves as a "decision trigger"—reminding experienced business owners to implement strategies they already know but might forget under stress.

Weather Agent: An advanced weather-specific agent with tool-use reasoning that will enable more flexible conversations about forecasts, allowing users to explore different locations and time periods without rigid input requirements.

Architecture Highlights

The system demonstrates best practices in AI agent development:

Modular Design: Reusable workflow components for user registration, alert subscriptions, and database operations ensure consistency and simplify maintenance.

Comprehensive Documentation: We documented 33 tools, prompts, and workflows within Voiceflow, created detailed database entity-relationship diagrams, and provided architecture overviews explaining all system components and their interactions.

Version Control: The entire codebase, including API functions and system configurations, is tracked in GitHub with clear commit history and pull request documentation.

Scalable Foundation: The flexible database schema, modular workflows, and well-documented architecture enable Atram AI to extend the system with new features and use cases.

Impact and User Insights

The pilot deployment revealed profound insights about how AI can serve vulnerable communities:

Business Intelligence, Not Just Data

Users transformed weather forecasts into strategic business advantages:

• Inventory Management: "I can order more products on Thursday if rain is expected on Saturday"
• Product Adaptation: "If I know it will be cold, I make more porridge because customers like it"
• Customer Communication: "When I know it will rain, I tell customers not to come"

Emotional Relief Through Certainty

The chatbot's value extended beyond information delivery to emotional support: "It has helped me to calm down and plan my activities better. I don't have to run around randomly hearing weather reports."

Users seek decision confidence, not just data. They want to know: "Can I stop worrying about getting caught in the rain?"

Trust Through Demonstrated Utility

Adoption followed a clear pattern:

1. Initial skepticism about AI technology
2. Introduction through trusted community members
3. Trial use driven by practical need
4. Conversion to active users after experiencing accurate forecasts
5. Deep trust formation when advice proved effective

The WhatsApp Advantage

WhatsApp proved essential for accessibility. Users don't distinguish between "using WhatsApp" and "using the chatbot"—they simply access weather information through familiar technology. This eliminated learning curves and leveraged existing trust in WhatsApp to transfer credibility to the AI service.

Room for Growth

The pilot also identified opportunities:

• Users primarily viewed the bot as a weather tool, not exploring broader AI capabilities
• Navigation complexity and system bugs occasionally frustrated users
• Some users wanted more detailed information, others preferred concise summaries
• Implementation guidance (e.g., "where exactly to place sandbags") would enhance preparedness advice

The project included:

1. Fully functional Voiceflow agent with agentic capabilities, custom functions, and knowledge base integration
2. Supabase database with flexible schema for user profiles, locations, and alert subscriptions
3. Weather forecast API hosted on Vercel, serving as a proxy to Open-Meteo with data transformation
4. WhatsApp deployment through FlowBridge with conversation flows optimized for mobile messaging
5. Airtable-based CMS for knowledge base content management with automated sync to Voiceflow
6. Complete documentation including architecture diagrams, entity-relationship models, setup instructions, and maintenance guidelines
7. Knowledge transfer session ensuring the Atram AI team can maintain and extend the system

Pilot Results

The chatbot demonstrated clear value in its core weather service:

Business Transformation: Users shifted from reactive crisis management to proactive business planning, using forecasts for strategic decisions about inventory, staffing, and operations.

Decision Confidence: The chatbot transferred the cognitive and emotional burden of weather uncertainty from users, providing clear, actionable conclusions rather than raw data requiring analysis.

Trust Building: Users who experienced accurate forecasts became advocates, demonstrating that AI adoption in underserved communities follows proven utility rather than technological sophistication.

Preparedness Activation: Flood advice served as "decision triggers"—prompting experienced business owners to implement protective measures they knew about but hadn't prioritized during daily stress.

Accessibility Success: WhatsApp integration proved essential, enabling users with limited technical skills to access sophisticated AI services through familiar interfaces.

Key Success Factors

Agentic AI Design: Implementing intelligent conversation management rather than rigid scripts created natural, helpful interactions that adapted to user needs.

Platform Expertise: Deep knowledge of Voiceflow's capabilities—from custom functions to knowledge base optimization—maximized the platform's potential.

User-Centered Approach: Design decisions were informed by understanding users' actual contexts: high-stress situations, limited time, need for immediate actionable insights.

Comprehensive Documentation: Thorough documentation of architecture, workflows, and implementation decisions ensures long-term maintainability and enables future enhancements.

Accessible Technology: Choosing WhatsApp as the interface eliminated adoption barriers and leveraged existing user trust and familiarity.

Looking Forward

The foundation we built enables Atram AI to expand their impact:

Weather Agent Enhancement: The upcoming weather-specific agent with tool-use reasoning will enable more flexible, conversational forecast exploration.

Expanded Knowledge Base: The modular CMS approach allows continuous addition of new preparedness topics and tactics based on user feedback and evolving needs.

Advanced Personalization: The database architecture supports storing user interaction history, enabling increasingly personalized advice and proactive recommendations.

Community Growth: The scalable infrastructure can serve growing user populations across multiple flood-prone regions.

Feature Evolution: The well-documented, modular codebase makes it straightforward to add new capabilities like community alerts, group coordination features, or integration with local emergency services.

This project demonstrates how thoughtful AI design, combined with deep technical expertise and genuine understanding of user needs, can create technology that truly serves vulnerable communities facing climate challenges.

Technical Deliverables