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IBM Nonprofit Australia

Food Ladder

Customer Service Automation

Food Ladder needed to scale greenhouse onboarding, school support and curriculum creation for hundreds of remote schools without relying on staff travel and manual lesson preparation, while preserving the personal guidance that defined the nonprofit's work.

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40 schools
85,000 meals supplemented from one greenhouse
Expects to scale from 132,480 meals in 2024 to 25 million meals in 2030
approximately 17,000 people

Reported impact

Business outcome

Food Ladder gave teachers and staff time back, improved the school experience, and created a scalable model that supports its plan to grow from tens of schools to a global network of AI-enabled food-production hubs.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Food Ladder worked with IBM Client Engineering to turn manual education and support workflows into an AI-powered operating model built on IBM watsonx.ai, IBM watsonx Assistant and IBM Granite models. The team first streamlined service requests so teachers could submit requests digitally instead of through after-hours phone calls and paperwork. IBM then built a chat agent to guide schools through greenhouse onboarding and answer common questions after equipment arrived, reducing staff intervention. For curriculum work, the team tested multiple large language models before selecting Granite because it stayed consistent with source material and context when generating unit plans, lesson plans and remixed content. That gave Food Ladder one AI stack for intake, teacher self-service and educational-content generation, helping the organization move from a hand-crafted support model to a much more scalable platform for school operations and learning.

Data flow

Teacher requests and onboarding questions enter digital workflows backed by watsonx Assistant, while curriculum inputs and remix requests are processed by Granite and watsonx.ai to generate classroom materials and guidance for schools using Food Ladder greenhouses.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx.ai Supports the AI workflows Food Ladder uses for request automation, onboarding assistance and educational-content generation.
    • IBM Granite Generates lesson plans and remixed educational materials while staying aligned to source content and curriculum context.
  2. Serving
    • IBM watsonx Assistant Provides the conversational agent that guides teachers through onboarding and routine support tasks.

Architecture clues

  • Food Ladder tested several LLMs before selecting Granite for lesson-plan generation.
  • IBM built the initial solution in approximately two and a half months.
  • The organization uses one watsonx-based stack across request intake, onboarding and content creation.

Evidence from the source

"Food Ladder currently operates in 40 schools"
"The team developed AI-powered solutions built with the IBM watsonx.ai AI studio and IBM watsonx Assistant"
"They finally chose IBM Granite"
"one greenhouse can supplement 85,000 meals"
"produce more than 25 million meals annually by 2030"
Transparency Evidence & methodology 75/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    Food Ladder customer story

    First-party
    https://www.ibm.com/case-studies/food-ladder
    Source type
    Vendor-published customer story
    Published
    Dec 20, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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