Give your team source-linked answers and the next step.

While the customer waits, your agent sees the answer, the policy it came from and the next action, and decides what to send.

Start with a support review How it works

My jacket arrived 26 days ago. Can I still return it?

Yes. You’re inside our 30-day return window, with four days left. I can send a return label now, or an exchange if you’d like a different size.

RET-13, effective Oct 15 · suggested reply, the agent approves

What the agent sees: a suggested reply with the policy it came from. They approve, edit or ignore it.

Sound familiar?

Agents search three places while the customer waits.
Newer agents ask the same colleague, and that colleague falls behind.
The right answer depends on which agent you got.

Time spent hunting for the answer is time not spent on the customer, and new agents learn from whoever they ask.

What changes.

TodayWith the engine

Finding the answer: Search three toolsShown with its source
Next step: The agent works it outSuggested, with the order and policy attached
New agents: Shadow a veteranCoached from the same sources everyone uses
Wrong answers: Found by a complaintFound when agents edit the suggestion

How it works. 5 steps, 4 stages.

AI2
Rules and automation1
Your team2
Approval

1Read
1. Rules and automation
  Pull the order, history and policy
  The context the agent would have gathered, in one view.
  - Shopify
  - Gorgias
2Suggest
1. AI
  Draft the answer and the next step
  Written from the sources in force, each one linked.
3Send
1. Your teamApproval
  Approve, edit or replace
  The agent decides what goes to the customer.
4Learn
1. AI
  Group the edits by cause
  Wrong source, missing fact or a coaching moment.
2. Your team
  Fix the source or coach
  Your CX lead acts on the pattern each week.

Agent edits show where a source is wrong

What you get. Real work, every week.

What the agent seesOrder O-8402

Signal	Source	Value
Delivered	Shopify	26 days ago
Return window	RET-13	30 days
Contacts this month	Helpdesk	None
Suggested next step	Policy	Offer a label or an exchange

The context behind the suggestion: the order, the policy and the next step, in one view.

Edited suggestionsThis week

Returns windowWording changed, policy kept4 editsFine
Waterproof claimAgent removed a claim we don’t make2 editsFix source
Shipping timesAgent added the carrier’s delay3 editsCoach

Where agents change a suggestion tells you where a source is wrong, or where coaching helps.

How you’ll know it’s working.

Answer accuracy against the source
Reviewed on a sample each week, by someone who owns the policy.
Time to a correct answer
How long the customer waits.
Adoption and edits, by tenure
Who uses it, and who corrects it.

Baselines agreed before we build.

Works with what you have.

Support

Gorgias
Zendesk
Intercom
Assembled

Commerce

Shopify

We configure first: Zendesk’s Copilot, Gorgias and Intercom suggest replies, and Assembled has its own copilot. We pick what fits your helpdesk, then write the sources and rules it uses.
We build when: The suggestions are fluent but can’t show their source, or can’t tell which policy applies.

What a first build includes.

Built in your accounts, around your team. Scope, fee and baseline agreed before we start.

1Design
2Build
3Run it, or hand it over

Start with a review Scope this build

We build

Source-linked suggestions inside your helpdesk
Rules for which policy applies to which order
A weekly edit review that finds wrong sources
Coaching examples drawn from real conversations

We start from

Helpdesk and Shopify access
Policies, macros and product facts
A month of conversations and agent edits

From your team

Owner: CX lead
Involves: Customer service
Quality
Engineering

Questions teams ask.

Does it actually help agents?

A peer-reviewed study of 5,172 support agents found AI suggestions raised issues resolved per hour by about 15% (Brynjolfsson, Li and Raymond, QJE 2025). Gains were largest for newer agents, with small quality declines among the most experienced. That was business-software support in 2020 and 2021, so we measure your own cohorts.

Will agents have to use it?

It works as a suggestion. Agents approve, edit or ignore it, and their edits are the signal that tells us where a source is wrong.

Who owns it once it’s running?

You do. It’s built in your accounts, documented, with training for the people who run it. Ownership usually sits with your CX lead. We can keep running it with you, or step back.

How do we start?

Usually with a support review: two to four weeks, a fixed fee credited to the build, and a plan that’s yours. If the job is already clear, we can scope the build directly.