OrderFlow AI — Case Study | Ibrahem Qusay
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Automation

OrderFlow AI

Intelligent Wholesale Order Intake and Processing

A designed system for turning unstructured wholesale order requests into reviewable, structured order workflows — currently a product concept and system design, not a deployed production system.

This page describes the designed architecture. Nothing on this page should be read as a claim that OrderFlow AI is deployed in production today.

At a Glance

Status:Product concept / system design
Category:Automation
Built by:Ibrahem Qusay
Proposed technologies:Speech transcription, structured LLM output, PostgreSQL, workflow orchestration

The problem

Distribution companies receive orders through WhatsApp, phone calls, social pages, sales reps, spreadsheets, and voice notes in Iraqi Arabic. Staff manually interpret products, quantities, and packaging units, check stock, apply pricing rules, and coordinate the warehouse — a process prone to delay, misread quantities, and weak auditability.

Example input

السلام عليكم، أريد للمطعم 15 كارتون ناجتس حار، و10 برغر دجاج، وإذا موجود دجاج 1 كيلو أضيفوا 20 كارتون. التوصيل لباجر للمنصور.

The design converts free-form requests like this into a structured draft order — never a final one — for human review.

Designed workflow

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Designed safety principles

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Planned interfaces

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Frequently Asked Questions

Is OrderFlow AI deployed?

No — OrderFlow AI is currently a product concept and system design, not a deployed production system. This page describes the designed architecture.

Would the AI ever finalize an order on its own?

No — the design requires explicit human approval before inventory is reserved and the warehouse is notified. Pricing, discounts, and delivery rules would be enforced by deterministic, auditable logic, not the model.

What happens when the system is unsure about a request?

The design routes unclear quantities, units, or products to a clarifying question instead of a finalized order — ambiguity is meant to be surfaced, never silently guessed.