The platform.
See it working.

Four steps from operating context to governed, measurable outcomes. Every decision audited, every action traceable.

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Retail runs on
decisions.
Most are ungoverned.

Every price change, every replenishment order, every markdown timing, every promotion selection — these are economic decisions that directly determine margin outcomes. Today, they're scattered across spreadsheets, disconnected SaaS tools, and institutional memory. There is no shared layer. No evidence trail. No learning loop.

Auctorian is that layer.

A single governing platform that ingests decision context, surfaces what you're losing, simulates action paths with full governance, executes within bounds, and learns from every outcome. Not another point solution. The operating system underneath all of them.

One command surface.
Six decision modes.

No maze of dashboards. The workspace supports both proactive opportunity surfacing and reactive user-driven decisions — through a single chat-first interface.

PROACTIVE
REACTIVE
DECISION WORKSPACE
SYNTHETIC
MODE: ANSWER MODEExplain evidence, signals, or runtime state

Where are we leaking margin this week?

A
Analyzing
Ask about decisions, plans, evidence...

How the Decision OS works.

Auctorian sits above existing enterprise systems and turns operating context into governed decisions, controlled action, and measured learning.

Connect the operating context.

Auctorian reads the business context required for decisioning — transactions, inventory, pricing, orders, suppliers, planning, and external conditions — without forcing a data warehouse rebuild.

Illustrative Context ViewSYNTHETIC SYSTEM MAP

Auctorian does not replace existing systems. It becomes the decision layer above them — reading context, not owning data.

ERPEnterprise transactions
CONTEXT READY
Inventory / WMSStock and warehouse context
CONTEXT READY
POS / CommerceSales and order data
CONTEXT READY
Orders / FulfillmentLogistics and delivery
CONTEXT READY
Supplier ContextPartner and compliance data
CONTEXT READY
Planning ContextDemand and financial plans
AVAILABLE
External ContextMarket and seasonal signals
AVAILABLE
Synthetic interfaceContext connected · Decision layer ready

The OS governs the lifecycle. Cartridges bring the domain intelligence.

CONTEXT → DECISION → GOVERNANCE → ACTION → OUTCOME

Three layers.
One governing core.

01

Signal Layer

Ingest. Normalize. Contextualize.

Translates raw enterprise data into structured decision context across every active operational domain.

02

Decision Runtime

Simulate. Approve. Execute.

Transforms business intent into governed recommendations with full evidence trails and safety bounds.

03

Trust Layer

Audit. Govern. Learn.

Enforces role-aware access, approval routing, dry-run verification, and continuous outcome measurement.

Architecture Principle

Auctorian separates conversation from authority. The chat interface coordinates the work; deterministic, source-backed decision infrastructure decides what is supportable.

One request.
Five governed agents.

One business request activates coordinated decision agents that understand context, retrieve evidence, generate actions, enforce governance, and render an auditable decision.

The model does not invent the decision. The runtime compiles it from evidence, candidates, constraints, approvals, and outcome logic.

Business Request

“Should we adjust pricing on underperforming SKUs in the bedding category?”

Decision Orchestrator
01

Understanding Agents

intentmodescopecontext
02

Evidence Agents

signalslineagefreshnessquality
03

Decision Agents

candidatesbaselinesimulaterank
04

Governance Agents

guardrailspermissionsrisk limitsapprovals
05

Presentation & Learning

briefevidence trailartifactoutcomes
Agent Group 01
ACTIVE

Understanding Agents

Interpret the user's intent, selected mode, business scope, active decision context, and follow-up questions.

intentmodescopecontext
FLOW
Runtime Output
EVIDENCE-BACKEDGUARDRAILEDAPPROVAL-READY

One auditable, review-ready decision.

Generic AI copilots produce answers. Auctorian coordinates governed decision agents to produce review-ready business decisions.

A simple decision,
end to end.

A retailer asks whether it can raise price on a single SKU. The numbers are illustrative; the flow is the decision logic Auctorian is designed to run.

Decision · Price Increase
ADVISORY MODE· ILLUSTRATIVE

Should we raise the price on this SKU?

Input Signals
Current price$49.99
Current cost$28.00
Weekly demand320 units
Candidate price$52.99
Elasticity assumption−1.2
Max increase guardrail7%
Candidates vs Baseline
Hold at $49.99BASELINE
$7,037 / wk
Raise to $52.99WINNER
$7,498 / wk+$461
Raise to $54.99BLOCKED
exceeds 7% guardrail
Raise to $52.99 — expected lift $461 / week
REVIEW-ONLY · DRY-RUN PACKET PREPARED
01

Input signals carry lineage and freshness

02

Guardrail prunes the unsafe candidate before simulation

03

Winner is scored against an explicit baseline

04

Decision remains review-only until approved

05

Execution prepared as a dry-run / manual action packet

Decisions
do not live alone.

A price move can change demand. A promotion can strain inventory. A stockout can waste ad spend. Auctorian coordinates decisions as a system, not as isolated recommendations.

Orchestration Loops

CROSS_PILLAR
ORCHESTRATED_LOOP
Pricing Shift
Replenishment Trigger

A commercial price adjustment changes velocity expectations, immediately calculating localized re-order levels to avoid stockouts.

[01] Trigger NodePrice adjustment mapped to expected velocity shift
[02] Decision PathLocal replenishment targets adjusted at regional hubs
[03] Governance RouteLocked within automated OTB financial bounds
[04] Outcome LearningVelocity monitored to reinforce replenishment trigger factors

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