Worked example · Order to cash

Trusted customers never wait. Risky orders get a person with the evidence.

Your biggest orders come from your best customers, and they are also where fraud costs the most. A high-value order review workflow tells the two apart in seconds, then writes every decision and its reasons back to the systems your teams already use.

QRION: High Order Value Workflow.Two real orders through the high-value order review workflow.
Transcript

Your biggest orders come from your best customers, and they're also where fraud costs you the most. Here's how QRION tells the two apart in seconds.

A $10,000 order arrives. Ship it blindly, and a stolen card becomes a chargeback. Hold every big order, and your best customers wait. The payment gateway sees a card. It can't see that this buyer has a deal open with your sales team.

This is the high value order review workflow. It starts the moment Shopify records an order, pulls that customer's order history from Shopify and their relationship from Salesforce.

Then it scores the risk and picks a lane. Low risk clears on its own. Anything uncertain goes to a fraud analyst, and nothing high risk is ever cleared automatically.

The score is in a black box. Every point has a reason. No account in the CRM, a first order, an unusually large basket. Points come off for a clean history, an open opportunity, a direct customer. Any decision can be explained in the record alone.

Shopify and Salesforce are connected once for the whole organization. Two of the more than 400 systems QRION works with. QRION subscribes to Shopify's order event itself, so every new order starts a review on its own.

Let's run two real orders. First, Sarah Mitchell, a long standing customer, orders two carbon kayaks, $9,500.

Seconds later, it's cleared. High value, yes, but Salesforce shows a direct customer with an open deal, so the score is zero. The decision and its reasons are written onto the order. Nobody had to touch it.

And Salesforce records it too, so Sarah's account team knows exactly what happened.

Now a first time buyer, three kayaks, over $14,000, no history, and no account.

This one goes to the fraud analyst review queue with the evidence attached.

The analyst sees what the workflow found, a score of 80 and exactly why. They check the buyer and approve.

The order now carries the decision, the score, every reason, and who made the call.

Trusted customers never wait. Risky orders get a person with the evidence, and every decision is explained and written back to the systems your teams already use. That's QRION.

What is a high-value order review workflow?

It is a governed process for flagged orders. A new Shopify order starts the run, the customer's history and relationship are pulled from Shopify, Salesforce and HubSpot, and a transparent risk score with reason codes routes the order: approve automatically, reject automatically, or send to a reviewer with every fact in one task. The decision fans out to all three systems and an audit record.

Systems
Shopify, Salesforce, HubSpot
Routing
Auto-approve, human review, or auto-reject
Controls
Second approver above an authority threshold; timed escalation
Model
Illustrative value of ~$360K a year; ~$287K from cost-side levers alone

Why the manual queue breaks

The review queue is where order acceptance goes wrong

  • Reviewers have no context

    Order, purchase history, account relationship and lifecycle stage live in four tabs. Assembling them takes 10 to 15 minutes, and different reviewers look at different things.

  • Decisions are slow and unbounded

    Queues have no service level. An order placed on Friday evening may sit until Monday, and the customer cancels or the shipment goes out late.

  • Outcomes do not propagate

    A reviewer approves in Shopify, and Salesforce and HubSpot never learn the order was questioned. The account team is blind to it.

The workflow

One flagged order, governed from intake to write-back

Step costs use the reference model: a $72,000 fully loaded analyst and 12 minutes of manual handling per review. Illustrative.

  1. 1
    System step

    Intake

    A Shopify order event starts one run per order, exactly once.

    Role
    Shopify
  2. 2
    System step

    Enrich

    Customer view from Shopify, Salesforce and HubSpot, queried in parallel.

    Role
    Reusable sub-workflow
  3. 3
    System step

    Score and route

    Weighted risk score, 0 to 100, with a reason code for every point.

    Role
    Scoring policy
  4. 4
    Human step

    Human review

    Every fact in one task, with a value-tiered service level.

    Role
    Review group
    Active time
    12 → 3.5 min
    Wait time
    6.5 h median today
    Cost per step
    ≈ $7 → $2
  5. Approval gate

    Second approver

    Above the authority threshold, a second person who is not the first.

    Role
    Manager group
  6. 6
    System step

    Write-back and audit

    Decision, score and reasons to all three systems and an immutable record.

    Role
    Shopify · CRM

Value model

About $360,000 a year, or $287,000 counting costs alone

Baseline: $120M annual sales, 400,000 orders, 4% flagged for review, $72,000 analyst cost, 35% contribution margin. Illustrative: plug in your numbers.

High-value order review illustrative value model
LeverBasisAnnual valueType
Review capacity released2,780 analyst hours a year: 55% of flagged orders auto-cleared, 12 → 3.5 minutes on the rest$96,000Cost
Fraud loss reduction20% of $420,000 in annual fraud and chargeback loss$84,000Cost
Integration build and maintenance avoided$200,000 custom build plus 20% a year upkeep, over three years$107,000Cost
False decline recovery700 orders recovered a year at $300 and a 35% margin$73,500Revenue
Audit and dispute evidenceEvery decision recorded with inputs, score, reasons and decision makerNot counted—
Total (cost-side floor in brackets)~$360,000 ($287,000)

Most organizations redeploy released hours rather than cut headcount: the same team absorbs seasonal peaks without contract staff. Run the review-capacity lever with your numbers.

Success measures

What the workflow is designed to move

Targets from the solution specification. The leading indicator is the human override rate: persistently high means the thresholds are wrong; near zero means review may be adding cost without adding judgment.

  • Thresholds become a business control. A fraud lead can move the auto-approve ceiling in the canvas and see the queue change the next day, without an engineering cycle.
  • The process becomes visible. A workflow on a canvas is legible to the people who own the process, not only to whoever wrote the integration code.
Baseline and target metrics
MeasureTodayTarget
Median time to decision6.5 hoursUnder 30 minutes
Orders requiring human review4.0% of ordersUnder 1.8%
Handling time per review12 minutesUnder 4 minutes
Fraud and chargeback loss0.35% of GMVUnder 0.28%
Orders lost to review delay0.5% of ordersUnder 0.32%

Questions

About this example

Is this a customer result?

No. It is a reference model built for a mid-market merchant profile: $120M in annual sales, 400,000 orders, 4% flagged for review. The formulas are what matter; replace the inputs with your own.

Does an AI model decide whether to ship an order?

No. The risk score is a transparent weighted sum with a reason code for every point. Low risk clears automatically, high risk is never cleared automatically, and anything uncertain goes to a person with the evidence attached.

Which systems does it use?

This example uses Shopify, Salesforce and HubSpot, each connected once for the whole organization. QRION works with more than 400 business systems, so the same pattern applies to other stacks.

Where else does this pattern apply?

Scored routing with a governed human decision and write-back to several systems has the same shape as credit approval, discount authorization, vendor onboarding, claims handling and contract exceptions.

Have a queue like this?

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Illustrative model. Plug in your numbers.