Demand Forecast Agent
Projects SKU-level demand by location
Tool: Demand forecast · Source: Order history
Industry
Retail and e-commerce AI agent operations
Catalogue, demand signals, store and warehouse stock, and service conversations
Operational layers in view
Catalogue dataDemand signalsStock positionsPricingService conversations
Demand Forecast Agent
Replenishment Agent
Catalogue Agent
Pricing Agent
Customer Service Agent
Fraud Agent
Node activity below is a demonstration using sample retail and e-commerce data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Retail and e-commerce
- Demand Forecast Agent
- Replenishment Agent
- Catalogue Agent
- Pricing Agent
- Customer Service Agent
- Fraud Agent
Deliverables planned
Dashboard
Charts
KPI panel
Data tables
Interactive map
Pauses for human approval
Commercial approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Retail and e-commerce AI agent operations centre
Workflow: Sales and stock data received → Monitor sell-through · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Replenishment Agent
Proposes transfer and purchase quantities
Tool: Demand forecast · Source: Order history
Catalogue Agent
Improves product content and attribution
Tool: Demand forecast · Source: Order history
Pricing Agent
Evaluates margin impact of price scenarios
Tool: Demand forecast · Source: Order history
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch retail and e-commerce agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Retail and e-commerce specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Demand Forecast Agent
Projects SKU-level demand by location
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Review advised
Replenishment Agent
Proposes transfer and purchase quantities
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Autonomous
Catalogue Agent
Improves product content and attribution
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Approval required
Pricing Agent
Evaluates margin impact of price scenarios
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Autonomous
Customer Service Agent
Drafts responses grounded in order and policy data
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Approval required
Fraud Agent
Flags abnormal ordering and refund behaviour
- Inputs:
- Order history, Inventory positions, Product catalogue
- Tools:
- Demand forecast, Replenishment planner, Content generator
Solution blueprint
Operational context
Industry challenges
- Operational data for retail and e-commerce is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Order history
- Inventory positions
- Product catalogue
- Support conversations
Controlled process
Example workflow
- Sales and stock data received
- Validate catalogue mapping
- Forecast demand
- Plan replenishment
- Evaluate pricing impact
Connection points
Integration opportunities
- E-commerce platform
- ERP inventory
- PIM
- Support desk
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Order history
- • Inventory positions
- • Product catalogue
- • Support conversations
Integrations
- • E-commerce platform
- • ERP inventory
- • PIM
- • Support desk
Outputs
- • Replenishment plan (CSV, PDF)
- • Demand forecast (CSV)
- • Commercial dashboard (Dashboard)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the retail and e-commerce agent team follows, from trigger to approved deliverable.
Lead qualification and outreach workflow
From a new lead to an approved outreach message and a CRM update.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. New lead: Trigger. A lead arrives from a form, import or CRM webhook.
- 2. Validate contact: Validation. Check required fields, duplicates and consent status.
- 3. Enrich lead: Approved tool. Look up firmographic context from approved research sources.
- 4. Score opportunity: AI agent. Rank fit and intent with an explainable score.
- 5. Draft outreach: AI agent. Write a personalised first message and follow-up sequence.
- 6. Salesperson approval: Human approval. The owning salesperson approves or edits before anything is sent. Human approval required.
- 7. Send message and update CRM: Action. Send the approved message and write the activity back to the CRM.
- 8. Pipeline update: Output. Scored lead, approved message and CRM activity record.
- 9. Track response: Monitoring. Watch replies and stage changes for the sequenced lead.
Approval gate: Salesperson approval. Deliverable: Table.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| sales-assistant | 9 | Yes. Policy: always | Table | Available | Available (signed in) | Available via Studio · requires ai_provider | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.