Catalyst AI

Productized Industry Solution

See what needs attention. Understand why. Act with control.

Retail Intelligence connects operational retail data and turns it into explainable recommendations for merchandising, planning, marketing, and store performance teams.

Discuss Retail Intelligence
Retail Intelligence illustration
Industry solution

The problem

Retail teams often have the data they need, but not in one decision flow. Sales, stock, campaigns, customer signals, store performance, and planning context live in different systems and reports. The result is manual analysis, repeated reconciliation, slower decisions, and too much dependence on a few experienced people.

What Retail Intelligence does

Unifies the operating view

Brings relevant sales, stock, campaign, store, SKU, and customer signals into a decision context.

Prioritises what matters

Highlights exceptions, changes, risks, and opportunities that deserve attention.

Explains recommendations

Shows the data and reasoning behind a recommendation so a planner or manager can assess it.

Coordinates action

Turns a recommendation into a task, brief, approval, or system action where the workflow is defined.

Builds operational memory

Keeps a record of prior decisions and outcomes so recurring issues become visible over time.

Example applications

  • Merchandising and replenishment review
  • Store and SKU performance
  • Campaign analysis
  • Stock-aware marketing decisions
  • Promotion planning
  • Exception management
  • Operational reporting and reconciliation

How it works

  1. 01

    Map the decision

    Define the business decision, source data, owner, and success measure.

  2. 02

    Connect the signals

    Bring together the minimum data required for that decision.

  3. 03

    Encode the logic

    Preserve deterministic business rules where they should remain deterministic; use AI where language, context, or pattern interpretation adds value.

  4. 04

    Validate

    Compare outputs against historical examples and domain-expert expectations.

  5. 05

    Deploy with control

    Introduce recommendations and actions with appropriate approval.

  6. 06

    Improve

    Measure outcomes and refine priorities, rules, and AI behaviour over time.

What it is not

  • Not a generic "AI for retail" platform that claims to solve every retail function.
  • Not an autonomous system that replaces merchandising judgement.
  • Not a dashboard with AI added as decoration.
  • Not dependent on one model vendor.

Governance

Recommendations should be explainable and actions should follow explicit boundaries. Where a decision affects material spend, inventory, external communication, or another high-impact outcome, the approval model should be designed with the client.

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Have a use case in mind?

Tell us where work is slow, fragmented, document-heavy, or difficult to scale.

Discuss Retail Intelligence