02Intelligence Layer

Your retail logic,
finally executable.

An AI that respects your metrics, your constraints, your priorities, and proposes one decision per SKU·store·action that you can trust, override, or auto-execute.

The intelligence problem

Generic AI doesn’t understand your retail business.

Off-the-shelf models optimize for averages. Retail decisions are about exceptions: the SKU·store pairs where the default answer is wrong.

Models don’t know your margins.

A forecast that ignores cost structure, MOQs and supplier lead times will route you toward the wrong buy every season.

Rules live in spreadsheets.

Minimum coverage by category, never-out-of-stock SKUs, exchange windows, encoded in one head and one Excel, applied inconsistently across teams.

Every team re-invents the wheel.

Buying computes margin one way, finance another, store managers a third. Nobody’s using the same definition of the same number.

How it works

Inside the Intelligence Layer.

Three stages. You configure the logic. The AI respects it.

01DefineActive

Build your metrics and signals.

Start from a pre-built retail library (sell-through, coverage, residual margin) and adapt the formulas to your reality. Every metric is shared across teams.

  • 60+ pre-built retail metrics
  • Full editor: formulas, aggregations, time windows
  • One definition, used by every team and every agent
Metric library · 24 active
Library24
$
Net Revenue
$
Days of Cover
%
Gross Margin
Sell Velocity
Stockout Risk
Markdown SensitivityActive

// custom retail metric

define markdown_sensitivity =

delta(units) / delta(price)

· weighted_by("season")

✓ Shared across all teams
02ConstrainActive

Encode your business rules.

Visual rule builder with no code. Hard constraints (never break MOQ), soft preferences (favor the donor store closest to demand), exception flows.

  • Pre-built templates for common retail rules
  • Versioned, with diff view and rollback
  • Bypass with reason, every override audited
Decision policy
Markdown · weighted policyPre-built template

4 signals composed into a single decision score per SKU

HardMargin floor≥ 38% gross
100%
SoftCover days≤ 45d target
72%
WeightTier sensitivityflagship × 1.4
48%
SoftSupplier window60d exchange
28%

Composed score per SKU

Weighted across signals · versioned · diffable

v2.4 saved
03RecommendActive

One decision per SKU·store·action.

The AI produces a single, explainable recommendation per item, ranked, with the reasoning and the alternatives. You approve, override, or auto-execute.

  • One recommendation per SKU·store, ranked
  • Full reasoning trace, every input visible
  • Override with reason; the model learns from it
Recommendation · explained
stockout-riskrule
overstockrule
Transfer

Transfer 12 units

Wool Coat · Lyon → Paris

+€2.4kEditOverride
top-sellerrule
margin floorrule

Every reco shows the rules behind it

Traceable, explainable · Override anytime — Solya learns

Capabilities

The logic center for every retail decision.

Six capabilities to encode, evaluate and trust the logic that runs your network.

Metric factory

Pre-built library + custom editor. One definition shared across teams and agents.

Signals & alerts

Trigger on threshold, trend, or anomaly. Routed to the right team, with context.

Business logic center

Visual rule builder, templates, versioning, rollback. No code required.

Constrained AI

Forecasts and optimization that respect hard rules and soft preferences.

Explainable decisions

Every recommendation comes with its reasoning trace. No black box.

Scenario simulation

Test a rule change on last season before shipping it.

Fit into the runtime

The logic center of the Solya runtime.

Logic computed in one place, applied everywhere: agents, apps, write-backs.

01

Data Layer

Unified retail model

02

Intelligence Layer

Decisions, your rules

You are here
03

Orchestration Layer

Agents that act

04

Application Layer

Role-specific apps

Retail was never built for AI.
We’re rebuilding it.

Last season we marked down €80k of stock in the wrong stores, and missed as much from stockouts. With Solya, both numbers are on the dashboard before the decision, not after.
MN

Monica Ndiaye

Head of Buying · 28 stores

Solya

Stop watching the dashboard.
Start running the decisions.

A 30-minute walkthrough on your own data shape. We’ll show what Solya would decide for your network this week.