CASE STUDY · ANONYMIZED

$10K/mo → $2,750/mo.

How a Shopify pet wellness brand cut customer support costs by 72.5% — without changing tools, processes, or brand voice. Same Gorgias desk. Same playbook. AI running it.

MONTHLY SUPPORT COST

Before

$10,000

After

$2,750

−72.5%

monthly support cost

SNAPSHOT

CLIENT

Anonymized — Shopify pet wellness brand, scaling DTC

ENGAGEMENT

AI customer service implementation via Opsio CS Co-pilot, on top of Gorgias

COST CHANGE

$10,000/mo → $2,750/mo (−72.5%)

FIRST RESPONSE

10 minutes

FULL RESOLUTION

45 minutes

CSAT

4.66 / 5

TOOLS CHANGED

None — same Gorgias desk, same integrations

CUTOVER

Minimal downtime, same voice + workflows preserved

THE SITUATION

A 5-figure-per-month support desk running on tribal knowledge

A growing Shopify pet wellness brand was spending ~$10,000 per month running customer support. The function worked. CSAT was decent. Customers got answers. But the operation was expensive, and every month the bill kept growing as ticket volume scaled with revenue.

The CX knowledge lived in people’s heads and across 8–9 separate process docs, with a stack of integrated Shopify and helpdesk apps each running their own micro-workflows. Everything depended on whoever was staffing the desk knowing the unwritten rules: which refund threshold needed manager approval, how to phrase the swap-vs-refund script for a damaged item, which escalation triggers required a same-day call.

WHAT WAS ACTUALLY BROKEN

Not the tooling. The documentation gap.

Their Gorgias setup was fine. The integrations were fine. The agents were good. What was broken was something most brands have but never look at directly: their CX knowledge was scattered and undocumented in a way that made AI automation impossible.

You can’t hand an AI agent a process it can’t read. The bots most brands try first fail not because the bots are dumb — they fail because the brand’s rules, voice, edge cases, and escalation logic live in 8 docs, 4 Slack channels, and the head of the senior support lead.

That was the real problem. And it’s the work nobody wants to do, because it’s tedious, structural, and doesn’t feel like “AI work.” It’s the foundation that makes the AI work.

WHAT WE DID

Documented the playbook first. Layered AI second.

1. Brand Brain consolidation

Before touching automation, we extracted everything into one structured CX knowledge base — the Brand Brain:

  • The 8–9 process docs consolidated into one canonical source
  • App workflows and integration logic written down explicitly
  • Tribal knowledge interviewed out of the senior support lead
  • Voice and tone guide — how the brand actually sounds at scale
  • Edge-case handling with the real rules (refund thresholds, swap-vs-refund triggers, damaged-product playbook)
  • Escalation rules — who, when, and what counts as a same-day case

    • That extraction was the project. Not the technology. The work was making the brand’s CX legible — turning what worked in human heads into something an agent could read, follow, and stay accurate against.

    2. AI execution layer via Opsio

    With the Brand Brain in place, we layered AI execution directly onto their existing Gorgias desk via Opsio CS Co-pilot — our Chrome extension that drafts on-brand replies grounded in each brand’s knowledge base.

    The agent ran their documented playbook, not a generic CX script. Every draft was sourced from the Brand Brain, so the voice held, the policies stayed accurate, and the edge cases got handled the same way a senior human would have handled them — because a senior human had documented exactly how.

    Humans stayed in the loop on approval. The confidence score on every draft flagged uncertain cases to a human reviewer instead of pushing them through. The setup cutover with minimal downtime — same workflows, same voice, no rebuild.

    RESULTS

    72.5% cost reduction, faster response, higher CSAT

    The brand kept its same Gorgias desk, same integrations, same brand voice. What changed was who was running the playbook — and how legible that playbook was to the AI doing it.

    No rebuild. No app migration. No CSAT crash. Just a documented brand running its own documented playbook at a fraction of the cost.

    WHY IT WORKED

    The agent was the easy part.

    The work was making the brand’s CX legible enough for AI to run it reliably. The documentation. The voice. The edge cases. The escalation logic. The homework most brands never do.

    Once that was in place, the AI layer was almost anti-climactic — drop the agent on top, point it at the Brain, and let it run. That’s why this case held its CSAT and dropped its cost at the same time: the AI wasn’t guessing. It was reading.

    AI customer service doesn’t fail because the AI is bad. It fails because the brand isn’t documented well enough for any agent — human or AI — to run it without improvising. Fix the documentation. The cost reduction follows.

    RELATED

    Go deeper

  • How to cut Shopify customer support costs — the same playbook, generalized
  • Why most AI customer service bots fail Shopify brands
  • Done-for-you AI customer service for Shopify brands
  • The Brand Brain methodology
  • The AI Workflows pillar

    • Want this for your Shopify store?

    We’ll audit your support cost, your knowledge base, and your highest-volume ticket types live on the call — and tell you exactly what the AI cutover would look like for your brand.