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Salesforce Retail United States

ezCater

Customer Service Automation

ezCater needed to handle increasingly complex workplace food orders, last-minute changes, and hybrid-work demand swings without forcing customers or service staff through manual, error-prone update workflows.

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Nearly two decades of catering data

Reported impact

Business outcome

ezCater expects smoother ordering for customers, fewer errors during complex modifications, better demand forecasting, and more time for reps to focus on truly exceptional or sensitive issues.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

ezCater is using Agentforce with Data Cloud, Service Cloud, Sales Cloud, and Slack to turn group-food ordering into a conversational, data-driven workflow that can adapt to modern workplace complexity. Instead of requiring customers to navigate forms for every dietary change, venue update, or reorder, Agentforce accepts natural-language requests and translates them into structured actions that update the order and, when needed, escalate the work to a human representative with the right context. Data Cloud centralizes millions of workplace food-order records so the agent can recommend restaurants, compose combinations that fit headcount and dietary constraints, and improve forecasting for future demand surges. The same architecture extends into Slack, where ezCater plans to let workplace teams poll preferences, adjust orders, and share feedback from inside a public app, keeping customer interaction, data, and follow-up on one connected platform.

Data flow

Customer requests are captured in natural language, Agentforce uses Data Cloud order history to recommend menus or apply changes, structured order details are passed into ezCater systems or escalated through Service Cloud, and Slack extends the workflow to workplace teams for collaboration and feedback.

Solution architecture

5 components · 3 layers
  1. Storage
    • Data Cloud Unifies historical order data so the agent can recommend restaurants and forecast demand.
  2. Serving
    • Service Cloud Provides the handoff destination for issues that need a human representative.
    • Sales Cloud Stores related customer and account context used by the broader ezCater workflow.
    • Slack Supports the planned ordering app where teams can interact with meal workflows directly.
  3. Orchestration
    • Agentforce Accepts natural-language food-order requests, updates orders, and escalates complex issues.

Architecture clues

  • Agentforce converts conversational requests into structured order inputs for ezCater systems.
  • Data Cloud is intended to centralize order history and support predictive demand modeling.
  • Slack is a customer-facing extension point for ordering and preference polling.

Evidence from the source

Agentforce will automatically adjust the orders, making the process as intuitive as speaking to a human.
By leveraging data from millions of workplace food orders, Agentforce will enable the virtual agent to provide tailored suggestions.
Using predictive models on Salesforce Data Cloud, ezCater will anticipate and prepare for new orders.
ezCater plans to launch a public app in the Slack App Marketplace.
Transparency Evidence & methodology 71/100 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 value71/100
  • 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 specificity16/20
    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
    4 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. Salesforce

    ezCater customer story

    First-party
    https://www.salesforce.com/customer-stories/ezcater/
    Source type
    Vendor-published customer story
    Published
    Oct 10, 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

  • Published on source page Oct 10, 2024 · Salesforce · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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