Fleet Management

Persona

Role: Fleet systems developer at a delivery company operating 75 vehicles across three depots.

Vehicle maintenance is scheduled based on calendar reminders rather than actual mileage, fuel costs are tracked only at the fleet level with no per-vehicle breakdown, and there is no centralized record of vehicle condition or service history. When a vehicle breaks down, the operations team has no data to predict which vehicles are at risk.

Business Problem

Unexpected vehicle breakdowns disrupt delivery schedules and cost the company in towing fees, emergency repairs, and missed deliveries. Without mileage-based maintenance tracking, vehicles are either serviced too frequently (wasting money) or too infrequently (risking breakdowns). Fuel costs are rising but the company cannot identify which vehicles are inefficient. This scenario builds a fleet management workflow that tracks vehicle status and mileage, schedules maintenance based on usage data, and logs fuel consumption per vehicle.

Four-Step Application

This scenario works best as a four-step, human-in-the-loop application. The existing object model already gives this scenario a strong delivery backbone through Vehicle, MaintenanceRecord, and FuelLog.

• Mission metric focus: better on-time performance, lower exception costs, and higher operator productivity.
• Human + AI pattern: Each step combines structured workflow data with chat assistance, background generation, document understanding, and accessible interaction patterns when they improve the experience.

Step 1. Capture demand and context

• Goal: Make it easy for the user to start the Fleet Management journey with complete, trusted context.
• Required data: Vehicle context such as make, model, year, and mileage.
• AI support: Use chat to guide intake, generate clearer prompts, create accessible summaries, and assist with voice or vision-led capture when a form alone is not the best experience. EAI can support structured intake, chat workflows, and document-centred capture today; richer native multimodal capture may still need workflow extensions or connected services.
• Business impact: Improve completion rate, reduce first-touch effort, and raise customer or staff confidence in the UX from the very first interaction.
• EAI delivery: Model the intake as tenant-isolated object types and resources, then use actions, chat workflows, and document indexing or classification to keep the initial record complete and usable.

Step 2. Prepare the decision

• Goal: Turn the captured context into the next best action for Fleet Management without forcing the human reviewer to assemble the case manually.
• Required data: Vehicle state and history; MaintenanceRecord fields such as type, date, cost, and vendor.
• AI support: Run background summarisation, extraction, classification, recommendation drafting, and answer generation so a reviewer sees a prepared case instead of raw fragments. EAI delivers the structured records and AI workflow hooks for this today; specialised scoring engines, external rules, or advanced reasoning controls may still need integration work.
• Business impact: Reduce cycle time, improve quality and consistency, and protect the mission-critical metric before the case moves into execution.
• EAI delivery: Link records across the scenario, persist decision state as resources, and use workflow actions plus chat assistance to keep humans in control while AI prepares the work.

Step 3. Execute and collaborate

• Goal: Coordinate the actual work, handoffs, approvals, and user updates needed to deliver the service or outcome.
• Required data: MaintenanceRecord execution state; FuelLog fields such as date, gallons, cost, and odometer.
• AI support: Draft replies, produce work packets, monitor exceptions in the background, and surface the next action for each operator. EAI can orchestrate tenant-isolated records, actions, chats, and document workflows today; deeper system-to-system automation may require additional connectors or workflow capability.
• Business impact: Increase operator productivity, reduce rework across handoffs, and improve service consistency across the application journey.
• EAI delivery: Use linked object types, actions, resource updates, and workflow-triggered AI assistance so the team can execute in one model instead of splitting work across disconnected tools.

Step 4. Resolve, explain, and improve

• Goal: Close the loop with a clear outcome, an understandable explanation, and feedback that improves the next case.
• Required data: final status, outcome, audit history, and follow-up signals across Vehicle, MaintenanceRecord, and FuelLog.
• AI support: Generate outcome summaries, customer-friendly answers, compliance-ready notes, management insights, and accessible follow-up content. EAI can store outcome records and support answer generation today, while richer proactive agents, advanced analytics, or channel-specific accessibility features may need additional product capability.
• Business impact: Increase trust, quality, and measurable business value through better on-time performance, lower exception costs, and higher operator productivity.
• EAI delivery: Keep the full audit trail in structured resources, use AI workflows to explain outcomes, and feed the resulting signals into future product, service, and operational improvement work.

EAI Platform Support By Step

EAI provides the safe service boundary for Fleet Management through Object Types, tenant-scoped resources, document processing, chat workflows, and CLI verification. For this scenario, the main records are Vehicle, MaintenanceRecord, and FuelLog.

Process step	What EAI provides	Calling pattern
Step 1. Capture demand and context	Tenant-scoped intake resources for Vehicle context such as make, model, year, and mileage. Object Type validation, starter forms, optional document intake, and chat-guided capture keep the first record complete.	Define fields in src/eai.config/object-types.ts, run eai types validate and eai types seed, create initial Vehicle records with useResources('Vehicle') or eai resources create Vehicle, and keep browser calls behind /api/eai/....
Step 2. Prepare the decision	Linked resource queries over Vehicle state and history; MaintenanceRecord fields such as type, date, cost, and vendor. Search, schema checks, document classification or RAG indexing, and chat summaries turn raw context into a prepared decision.	Use useResources('Vehicle') list/query/search patterns, verify shape with eai resources schema, use useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index where supporting material exists, and send decision-support prompts through useChat(workflowId, 'chat') or eai chat send.
Step 3. Execute and collaborate	Resource updates and actions for MaintenanceRecord execution state; FuelLog fields such as date, gallons, cost, and odometer. Status changes, assignments, notes, generated work packets, and chat support keep humans in control during execution.	Model actions in the Object Type code, call client.resources.executeAction(type, id, action) or the app hook equivalent, update records through the app service layer, and verify with eai resources get/list/query.
Step 4. Resolve, explain, and improve	Outcome resources for final status, outcome, audit history, and follow-up signals across Vehicle, MaintenanceRecord, and FuelLog. Audit-friendly links, indexed final documents, reporting snapshots, and answer generation make the result explainable and reusable.	Persist outcomes as resources, index final material with eai docs index or useDocuments().ragIndex, send explanation prompts with useChat or eai chat stream, and use eai resources aggregate/search for reporting checks.

Prompt, Code, And Service Pattern Mapping

The Object Type code example on this page is the implementation contract for the EAI platform services. eai-gofer should read that code as the source of truth for which resource, document, and chat calls belong in the app.

Use this prompt shape when asking eai-gofer or another coding agent to implement the scenario:

Use the EAI App Template. Model Fleet Management with Object Types for Vehicle, MaintenanceRecord, FuelLog. Use useResources for records and actions, useDocuments for uploads/classification/RAG where documents appear, useChat for workflow assistance, and verify with eai types/resources/docs/chat commands. Use eai publicapi only when no named command covers the required platform call.

Scenario artifact	How it maps to EAI service calls
Four-step process	Step 1 becomes resource creation, Step 2 becomes resource query/search plus optional document or chat preparation, Step 3 becomes resource update/action calls, and Step 4 becomes outcome persistence plus explanation/reporting calls.
Object Type definitions	eai types validate, eai types seed, and eai resources schema make the model available and checkable before UI work starts.
Properties and indexes	Fields become useResources payloads, filters, list views, and eai resources create/list/query/search checks. Indexed fields should support lookup and triage, not duplicate canonical records.
Links between Object Types	Relationships become linked-resource UI, timeline context, and audit trails that app code loads through resource queries rather than separate bespoke stores.
Actions and status fields	Workflow buttons and operator transitions call resource action/update helpers, then verify state with eai resources get/list/query.
Document and chat prompts	Prompts should call the platform documents and chat patterns: useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index for documents, and useChat, eai chat send, or eai chat stream for conversational assistance.

Object Types

Name	Key Properties	Links	Actions
Vehicle	make (text), model (text), year (number), mileage (number), status (select: active, maintenance, retired)	one-to-many → MaintenanceRecord, one-to-many → FuelLog	--
MaintenanceRecord	type (select: oil-change, tires, brakes, inspection, repair), date (date), cost (number), vendor (text), mileageAtService (number)	many-to-one → Vehicle	--
FuelLog	date (date), gallons (number), cost (number), odometer (number)	many-to-one → Vehicle	--

CLI Workflow

1. Scaffold the project

   eai init fleet-management

2. Authenticate and pull environment

   eai login
   eai env pull --include-secrets

   If you are an external developer, see [Configuration](/docs/configuration) for login and local environment setup.

3. Define your Object Types

   Create the Vehicle, MaintenanceRecord, and FuelLog types in src/eai.config/object-types.ts (see code example below).

4. Validate the type definitions

   eai types validate

   Tenant: fleet-management
   ✔ Vehicle — 5 props, 2 links, 0 actions
   ✔ MaintenanceRecord — 5 props, 1 link, 0 actions
   ✔ FuelLog — 4 props, 1 link, 0 actions
   
   ✔ All Object Types are valid

5. Seed types to the platform

   eai types seed

6. Create sample resources

   eai resources create Vehicle --data '{"make": "Ford", "model": "Transit 250", "year": 2023, "mileage": 34500, "status": "active"}'

7. Start local development

   eai dev

Code Example

// src/eai.config/object-types.ts
export const objectTypes = {
  'fleet-management': [
    {
      name: 'Vehicle',
      displayName: 'Vehicle',
      description: 'A vehicle in the company fleet',
      properties: [
        { name: 'make', type: 'text' as const, required: true },
        { name: 'model', type: 'text' as const, required: true },
        { name: 'year', type: 'number' as const, required: true },
        { name: 'mileage', type: 'number' as const, required: true },
        { name: 'status', type: 'select' as const, required: true, defaultValue: 'active', options: [
          { label: 'Active', value: 'active' },
          { label: 'Maintenance', value: 'maintenance' },
          { label: 'Retired', value: 'retired' },
        ]},
      ],
      linkTypes: [
        { name: 'maintenanceRecords', targetObjectType: 'MaintenanceRecord', cardinality: 'one-to-many' as const },
        { name: 'fuelLogs', targetObjectType: 'FuelLog', cardinality: 'one-to-many' as const },
      ],
      actions: [],
      status: 'published' as const,
    },
    {
      name: 'MaintenanceRecord',
      displayName: 'Maintenance Record',
      description: 'A service or repair performed on a fleet vehicle',
      properties: [
        { name: 'type', type: 'select' as const, required: true, options: [
          { label: 'Oil Change', value: 'oil-change' },
          { label: 'Tires', value: 'tires' },
          { label: 'Brakes', value: 'brakes' },
          { label: 'Inspection', value: 'inspection' },
          { label: 'Repair', value: 'repair' },
        ]},
        { name: 'date', type: 'date' as const, required: true, indexed: true },
        { name: 'cost', type: 'number' as const, required: true },
        { name: 'vendor', type: 'text' as const, required: true },
        { name: 'mileageAtService', type: 'number' as const, required: true },
      ],
      linkTypes: [
        { name: 'vehicle', targetObjectType: 'Vehicle', cardinality: 'many-to-one' as const },
      ],
      actions: [],
      status: 'published' as const,
    },
    {
      name: 'FuelLog',
      displayName: 'Fuel Log',
      description: 'A fuel fill-up record for a fleet vehicle',
      properties: [
        { name: 'date', type: 'date' as const, required: true, indexed: true },
        { name: 'gallons', type: 'number' as const, required: true },
        { name: 'cost', type: 'number' as const, required: true },
        { name: 'odometer', type: 'number' as const, required: true },
      ],
      linkTypes: [
        { name: 'vehicle', targetObjectType: 'Vehicle', cardinality: 'many-to-one' as const },
      ],
      actions: [],
      status: 'published' as const,
    },
  ],
};

Key Takeaways

• Mileage-based maintenance: Recording mileage at each service enables scheduling based on actual usage rather than arbitrary calendar intervals.
• Per-vehicle cost analysis: Maintenance costs and fuel consumption tracked per vehicle reveal which units are expensive to operate and may be candidates for replacement.
• Fuel efficiency monitoring: Odometer readings on fuel logs enable calculation of miles-per-gallon trends, identifying vehicles with declining efficiency.
• Complete vehicle history: Linking maintenance records and fuel logs to vehicles creates a comprehensive service history that supports resale value documentation and warranty claims.