Portfolio project · Support operations

AI Support Resolution

I designed the eligible support scenarios, policy boundary, escalation rules, and response voice for this workflow. It resolves routine questions in a professional, warm tone and routes anything sensitive, uncertain, or consequential to the right person.

Auto-reply example

Maya · order #2184

“My order is three days past the delivery estimate. Can you check it?”

Reply sent in the simulation

Hi, Maya!

We're very sorry that your order #2184 is taking a little longer to reach you. The carrier's estimated delivery date has passed, and we're following the shipment closely to understand what happened.

As soon as we have an update, we'll be in touch. Thank you for your patience while we resolve this. 🤍

Personal, but bounded. The reply uses the customer name, order reference, and an approved policy fact. It does not promise an outcome or make an order change.

What the workflow delivered

Routine questions resolved without waiting

For eligible conversations, the workflow retrieves approved store information, personalizes a clear reply, and sends it through the support channel. Everything involving an action, risk, or uncertainty goes to the team.

120Tickets processed

72Auto-reply eligible

48Human review

8Approved policies

Prototype boundary: the case simulates 120 support tickets and eight policies. No live Shopify store, customer, order, or message was connected. “Sent” means sent in the synthetic run only.

01 / Context

Support slows down when routine questions wait in the same queue as real exceptions

The operational bottleneck is not simply writing a message. It is separating questions that can be answered safely from requests that require judgment, an account action, or more evidence.

Who is affected

Customers with a simple policy question should not wait for an agent. The team needs more time for shipment issues, refunds, cancellations, and other exceptions.

Why approved sources come first

Every reply starts with a current policy, product detail, or order fact. The workflow does not rely on the model’s memory or invent a policy claim.

What stays with a person

Refunds, cancellations, replacements, address changes, payment data, legal or safety concerns, low confidence, and conflicting evidence stay in human review.

02 / Solution

Resolve the routine question, then protect the exception path

01Read
Identify the customer and question
Keep the message, customer name, order reference, channel, and relevant order facts together.
02Check
Find approved evidence
Retrieve the current policy and test confidence, policy coverage, and the allowed reply type.
03Resolve
Send a warm, careful reply
For an eligible routine question, personalize a clear reply and send it through Shopify Inbox.
04Escalate
Route exceptions to the team
Keep actions, money, sensitive data, risk, missing evidence, and ambiguity with a person.

03 / Evidence

Inspect the reply log, source policy, and human queue

The synthetic run separates eligible auto-replies from the human queue. Every response retains the policy source and the reason it was sent or escalated.

Quick view the synthetic policy reference ↗

04 / Production

Autonomy would expand only when the reply quality proves reliable

Business measures

Resolved conversations, first-response time, customer re-contact rate, handoff rate, and time released for the support team

Quality measures

Grounded-reply rate, human corrections, incorrect sends, escalation precision, policy coverage, and customer satisfaction

Governance

Approved reply categories, policy owners, release checks, audit logs, sampling, kill switch, and a human owner for every exception

Production path: Shopify Inbox can assign its Inbox agent to customer conversations and use store policies, catalog, knowledge-base facts, and storefront content as sources. This portfolio prototype does not connect to Shopify. Read the Shopify Inbox overview ↗ · Read about instant answers ↗ · Read the NIST measurement guidance ↗