Rhubarb – AI-Powered
Gardening Assistant

Smarter gardening with
AI-driven support

Most home gardeners struggle with guesswork, watering schedules, pest prevention, and planting decisions often lead to wasted effort, failed crops, and frustration.

Quokka Labs partnered with Rhubarb to build Ruby, an AI-powered gardening assistant that acts as a personal guide. Unlike static guides or generic tips, Ruby provides context-aware, hyper-local advice, automates routine tasks, and offers real-time support, helping users grow more with less effort.

It saved gardeners hours every week, reduced wasted resources, and boosted confidence, leading to higher adoption and satisfaction across the platform.

AI Services Powering Rhubarb

The Rhubarb Smart Gardener leverages a multi-agent AI system to deliver intelligent, context-aware notifications. Each agent is designed to handle a specific part of the notification workflow, ensuring accuracy, personalization, and scalability.

01 Conversational Agent (Ruby)

Context-aware chatbot + voice interface built on GPT, designed for natural, human-like interactions.

02 Retrieval-Augmented Generation (RAG)

Custom pipeline trained on expert gardening datasets, user forums, and localized content for precise answers.

03 Autonomous Task Agents

Specialized AI agents handle scheduling, reminders, and care routines without manual setup.

04 Predictive AI Models

Machine learning forecasts for environmental risks (frost, drought, pests) with proactive alerts.

05 Personalization Engine

Gets smarter with every interaction, refining its responses based on real user behavior.

06 Continuous Learning System

AI refines recommendations by analyzing user feedback, interactions, and outcomes over time.

01

Conversational Agent (Ruby)

Context-aware chatbot + voice interface built on GPT, designed for natural, human-like interactions.

02

Retrieval-Augmented Generation
(RAG)

Custom pipeline trained on expert gardening datasets, user forums, and localized content for precise answers.

03

Autonomous Task Agents

Specialized AI agents handle scheduling, reminders, and care routines without manual setup.

04

Predictive AI Models

Machine learning forecasts for environmental risks (frost, drought, pests) with proactive alerts.

05

Personalization Engine

Gets smarter with every interaction, refining its responses based on real user behavior.

06

Continuous Learning System

AI refines recommendations by analyzing user feedback, interactions, and outcomes over time.

Smart AI Solutions to Common Challenges

Rhubarb automates personalized, context-aware notifications, delivering timely alerts while reducing manual effort for gardeners.

01

Problem

Gardeners waste hours searching online for generic advice.

AI Solution

Ruby AI chatbot provides personalized, hyper-local answers instantly, with voice-enabled assistance.

02

Problem

Weather unpredictability often ruins crops.

AI Solution

AI-suggested primary and secondary AI-based insights give proactive alerts and tailored care recommendations (e.g., watering before a heatwave).

03

Problem

Pest infestations are detected too late, causing crop loss.

AI Solution

AI pest detection & prevention provides early warnings and region-specific remedies.

04

Problem

Gardening requires constant manual task tracking

AI Solution

AI task automation sends reminders for watering, pruning, fertilizing, and harvesting, removing guesswork.

05

Problem

Lack of expert guidance for unique plant or soil issues.

AI Solution

AI recommends that local professional users can chat with or invite into their gardening groups.

Rhubarb vs Traditional Gardening Assistants

Users receive timely, meaningful alerts suited to their needs, while the system operates efficiently behind the scenes.

Traditional Gardening Assistants

Rule-based tips → limited to generic guides and static FAQs.
Cannot adapt to different plants, soils, or climates.
Users search manually → inconsistent and time-consuming results.
No predictive alerts for weather or pests → gardeners react too late.
Manual tracking required for watering, fertilizing, and harvesting.

AI-Powered Gardening Assistant

Understands natural language & context → Ruby chatbot answers personalized questions instantly..
Adaptive learning → guidance evolves with soil type, region, and plant growth stage.
Conversational & voice-enabled → feels like chatting with a gardening expert.
Predictive AI alerts → warns about frost, droughts, and pests before they damage crops.
Automated reminders → watering, fertilizing, and harvesting tasks handled without guesswork.

The Outcomes We Delivered

Clear and measurable outcomes, delivering faster development, smarter automation, and healthier harvests.

100%

automated reminders

40%

faster product delivery

50%

fewer crop failures

27%

higher harvest yields

Timeline

Our AI Workflow vs Traditional Development

Total: 12–16 weeks

Total: 6 weeks
(Cut delivery time nearly in half)

Traditional Gardening
Assistants

Phase 1
Discovery & design →
3–4 weeks
Phase 2
Development & prototyping →
5–6 weeks
Phase 3
Integration & testing →
4–6 weeks

With Quokka Labs’ AI
Workflow

Phase 1
Discovery & design →
1 week (≈70% faster)
Phase 2
Development & prototyping →
2 weeks (≈50% faster)
Phase 3
Integration & deployment →
3 weeks (≈50% faster)

VS

Total: 5 weeks (Cut delivery time in half)

Smart Technologies for Smart Gardener

From data queries to real-time delivery, these tools formed the backbone of Rhubarb’s intelligent gardening assistant.

AI Layer

OpenAI GPT based + RAG pipelines smart assistant.

Backend

Python + Django for scalable system performance.

Agentic Framework

LangChain for orchestrating AI-driven workflows and agents.

Database

PostgreSQL for reliable, structured data management.

Our AI Consultation Approach

Every project begins with understanding your needs, challenges, and goals. Here’s how we shaped Rhubarb into a smart, scalable assistant.

Assessment

We analyzed user behavior, gardening workflows, and data dependencies to design a system that drives real, measurable outcomes.

Identified gardener workflows and how notifications could simplify plant care routines.

Mapped plant types, seasonal cycles, and task frequency to trigger relevant alerts.

Reviewed APIs for weather, plant data, and user calendars for accuracy and reliability.

Outlined success measures including engagement rate, reduced manual effort, and notification accuracy.

Training

We enabled both teams and users to adapt quickly by providing structured onboarding, system walkthroughs, and usage guidelines.

Conducted workshops to explain agent workflows and their role in notification delivery.

Built easy guides for gardeners on setting preferences and customizing notifications.

Trained users with simulated alerts for weather-based, seasonal, and location-specific triggers.

Gathered feedback from test groups to refine accuracy and user satisfaction.

Implementation

We translated validated designs into a functioning AI-driven system through iterative development, testing, and phased rollout.

Built early versions of the notification engine for internal testing and validation.

Created, tested, and improved alert templates for clarity, personalization, and timing.

Connected weather, plant, and scheduling APIs into the system for real-time data enrichment.

Launched with a small group of users to test scalability and real-world effectiveness.

Planned a stepwise rollout ensuring smooth adoption and minimal disruptions for all users.

Rhubarb – AI-Powered Gardening Assistant

Most home gardeners struggle with guesswork, watering schedules, pest prevention, and planting decisions often lead to wasted effort, failed crops, and frustration.

Quokka Labs partnered with Rhubarb to build Ruby, an AI-powered gardening assistant that acts as a personal guide. Unlike static guides or generic tips, Ruby provides context-aware, hyper-local advice, automates routine tasks, and offers real-time support, helping users grow more with less effort.

It saved gardeners hours every week, reduced wasted resources, and boosted confidence, leading to higher adoption and satisfaction across the platform.

AI Services Powering Rhubarb

01

Conversational Agent (Ruby)

02

Retrieval-Augmented Generation (RAG)

03

Autonomous Task Agents

04

Predictive AI Models

05

Personalization Engine

06

Continuous Learning System

01

Conversational Agent (Ruby)

02

Retrieval-Augmented Generation (RAG)

03

Autonomous Task Agents

04

Predictive AI Models

05

Personalization Engine

06

Continuous Learning System

Smart AI Solutions to Common Challenges

01

Problem

AI Solution

02

Problem

03

Problem

AI Solution

04

Problem

05

Problem

AI Solution

Rhubarb vs Traditional Gardening Assistants

Traditional Gardening Assistants

AI-Powered Gardening Assistant

The Outcomes We Delivered

Clear and measurable outcomes, delivering faster development, smarter automation, and healthier harvests.

100%

40%

50%

27%

Timeline

Our AI Workflow vs Traditional Development

Total: 12–16 weeks

Total: 6 weeks (Cut delivery time nearly in half)

Traditional Gardening Assistants

With Quokka Labs’ AI Workflow

VS

Total: 5 weeks (Cut delivery time in half)

Smart Technologies for Smart Gardener

AI Layer

Backend

Agentic Framework

Database

Our AI Consultation Approach

Assessment

User Needs Analysis

Use Case Mapping

Data Source Evaluation

Impact Metrics Definition

Training

Client Workshops

User Education

Scenario Testing

Feedback Collection

Implementation

Prototype Development

Template Refinement

API Integration

Rhubarb – AI-Powered
Gardening Assistant

Retrieval-Augmented Generation
(RAG)

Total: 6 weeks
(Cut delivery time nearly in half)

Traditional Gardening
Assistants

With Quokka Labs’ AI
Workflow