Built for Gunner. Private link.

Did you measure what the last stockout cost?

G1 Tan is your $700 kennel. When it runs out, the orders stop, and the cost lives in three places: missed revenue, margin, and ad spend pointed at a product nobody could buy.

Four questions. Every answer needs data from three tools. Nothing connects them.

Stock lives in Shopify. Reviews live in Yotpo. Spend lives in Meta. The answers exist. The mapping doesn't.

Stockouts
Did you measure what the last stockout cost?74 of your 485 variants are sold out right now, 15% of the catalog.
Reviews
Did you find the pattern in your negative reviews?4.8★ across 13,922 reviews, and the low ratings repeat the same themes for years.
Returns
Which channel causes the most $55 returns?Flat $55 return fee per kennel. No 1:1 exchanges.
Ads
Have you measured what a stockout costs in ads?Your ads from last October are still running.

The answers exist. The mapping doesn't.

Stock, reviews, returns, and spend each live in their own tool. Any one of them alone can't answer these questions. The cost only appears when they're connected.

Before

You know when G1 Tan ran out. You saw the orders stop.

After

You know what it cost: days out × velocity × margin × ad spend on a product nobody could buy.

Before

Reviews are read once and forgotten, or never read at all.

After

Patterns are counted over time: sizing, weight, durability. Each one is a return signal you see before the return.

Before

Returns pile up with a flat $55 fee and no pattern visible.

After

Return rate by product × channel, so you fix the source, not the symptom.

I connect the tools you already run: Shopify, Yotpo, Gorgias, your ads. Map the data into one layer. You don't build anything. You don't hire anyone. You just read the view.

Here's what that view looks like:

gunner · unified view

Revenue (mo)

$589K

Return rate

4.2%

Stockout risk

G1 Tan · 12d

OOS catalog

15.3%

Returns by channel

MetaGoogleOrganicEmail

Illustrative preview, built on public data. The real view uses yours.

The topping

An AI agent without your data is a party trick. With it, it's an analyst.

Same question, two answers. The only difference is what sits underneath.

AI, no data

"When does G1 Tan run out?"
I don't have access to your inventory data. I can give you general advice about inventory management, like setting safety stock levels or reorder points.

Generic. No numbers. No action. This is what an AI agent tells you when it's bolted onto nothing.

AI + your connected data

"When does G1 Tan run out?"
At current velocity, G1 Tan Large runs out in about 12 days. If Meta spend stays flat, that's roughly $18K in missed orders. Want the ad-pause draft and restock note?

Your numbers. Your forecast. Your action. Same AI, different layer underneath.

AI is the interface. The connected data layer is the answer. An agent on fragmented data fails. An agent on connected data is an analyst that never sleeps.

Three ways to start. No lock-in.

Diagnostic

$2,900 one-time

I find your leaks in 2 weeks. You keep the findings either way.

Most chosen

Managed layer

$6,500/month

All integrations, one view, weekly insights. Month-to-month, 30-day notice. Less than half the cost of one data engineer.

Performance

$4,500 + 10% of savings

Lower base, shared upside. You pay more only when I prove I saved you money.

An inhouse engineer costs $150-200K/year. But the whole layer costs $78K/year. And you can cancel next month with the developed setup and autonomous agents.

The math

June revenue: ~$589K. One percent recovered: $5,900. One avoided stockout month: $5-15K. The layer pays for itself before the invoice is due.

15 minutes. Your data, your numbers.

No pitch. I'll show you what your data says.

Book a call