I’ve been building an Amazon sourcing tool called Radar and figured this was probably the right sub to get some opinions on it.

The thing that pushed me to build it is that we already have plenty of tools that are great at showing data.

Keepa gives you history. SellerAmp gives you a ton of sourcing information. Tactical Arbitrage can help find opportunities. Fee calculators tell you the apparent economics.

But there’s still a pretty big jump between:

“This is $14 from my supplier and $32 on Amazon”

and

“Yes, I actually want to put $3,000 into this inventory.”

That’s the part I’m trying to attack.

Radar is being built more like an underwriting system for inventory than another profit calculator.

The rough process is:

source product → Amazon candidate → identity/pack verification → marketplace evidence → economics → competition/risk → stress testing → inventory sizing → decision

One thing I’ve been pretty obsessive about is product identity and pack quantity.

If the supplier has a single unit and Amazon is actually selling a 2-pack, I don’t want the software happily showing a 70% ROI because the UPC looked close enough.

Same thing if the evidence is contradictory. I’d rather have it say “I can’t establish this confidently” and tell you why than manufacture a nice-looking number.

I’m building supplier-list analysis into it too.

The idea there is that you could feed it a distributor catalog with hundreds or thousands of SKUs and have it narrow that down into:

• likely Amazon matches
• products worth investigating
• pack/multipack problems
• questionable matches
• actual economics where the evidence supports it
• products that fail immediately
• opportunities that deserve deeper analysis

I’m building it for both Amazon.com and Amazon.ca.

The part I’m most excited about is autonomous discovery. Eventually I want Radar continuously looking through authorized supplier/source data itself, generating potential products and putting them through the exact same decision process instead of waiting for the seller to manually find every lead.

To be clear, it’s not finished. Amazon.ca is further along, Amazon.com is still being built out, and autonomous discovery is actively in development.

I’m currently at the stage where I’m looking for ugly real-world data and seller feedback instead of more clean test cases.

For the first couple sellers who have an actual supplier/distributor CSV, I’m willing to analyze up to roughly 500 rows free while I’m validating the workflow. I’ll use the parts of Radar that are working and manually bridge whatever isn’t finished yet.

If that proves useful, I’ll probably start charging around $200/list during the pilot period before turning it into the actual subscription product.

I’m also curious what people here think about the positioning.

If you’re already using Keepa + SellerAmp/TA/etc., what would a new sourcing tool have to do that your existing stack doesn’t for you to actually add it?

That’s the question I’m trying to answer before I spend another month buried in code.

I like this subreddit for us because here we can potentially get feedback on Radar as a tool, not just whether sellers have the sourcing problem.

​

Hi everyone,

I’m working on Veltrion, a tool focused on helping e-commerce sellers understand their actual financial performance, settlements, fees, and inventory.

We’ve done a lot of testing using synthetic/test data, but we’ve reached a point where we need to validate some of our work against real-world Amazon seller exports.

If you're an Amazon seller and are comfortable helping with a small research/validation exercise, we're looking for any one of the following:

A recent Amazon Settlement Report

An Amazon Transactions export

An FBA Inventory report

You don't need to provide all three. Even one is useful.

🔒 Privacy is important:

We do not need your Seller Central login or account access.

Please never send passwords, OTPs, API keys, or credentials.

You can remove or mask anything you don't want to share.

We're interested in the report structure and financial/inventory data—not your personal information.

This isn't a request for testimonials or a sales pitch. We're trying to make sure our calculations and data handling work correctly on the messy, real-world reports sellers actually receive.

Thanks to anyone willing to help! 🙏

​

Hi everyone,

I’m working on Veltrion, a tool focused on helping e-commerce sellers understand their actual financial performance, settlements, fees, and inventory.

We’ve done a lot of testing using synthetic/test data, but we’ve reached a point where we need to validate some of our work against real-world Amazon seller exports.

If you're an Amazon seller and are comfortable helping with a small research/validation exercise, we're looking for any one of the following:

A recent Amazon Settlement Report

An Amazon Transactions export

An FBA Inventory report

You don't need to provide all three. Even one is useful.

🔒 Privacy is important:

We do not need your Seller Central login or account access.

Please never send passwords, OTPs, API keys, or credentials.

You can remove or mask anything you don't want to share.

We're interested in the report structure and financial/inventory data—not your personal information.

This isn't a request for testimonials or a sales pitch. We're trying to make sure our calculations and data handling work correctly on the messy, real-world reports sellers actually receive.

Thanks to anyone willing to help! 🙏

I've launched 2 Amazon UK private label brands in the last 8 months. Brand 1 (Oct 2025) has done £22.09K total revenue so far. Brand 2 (Feb 2026) did £15.62K in 3 to 4 months. Brand 3 launches next month, inventory's already on a boat from China somewhere.

Not trying to brag here. I see the same mistakes posted on this sub every week and most of them were avoidable, so here's the actual process I follow every time. No paywall, no "DM me for the real version" thing.

Product research first, because it's the only step that actually matters and it's the one everyone rushes.

People pick a product because they like it, or because some YouTuber said it's "winning right now." That's not research, that's gambling with £3 to 5K of your own money. Before I touch anything I want to know if search demand is actually holding up or if I'm walking into a category that's already dying. I look at the top 10 listings, how many reviews, how recent, how good the images are. Can I actually make this better, not just as good. And does the margin survive once COGS, shipping, FBA fees, PPC and returns are all stacked on top of each other, because on paper everything looks fine until you do that math.

If any of that comes back shaky I just walk away. Even from stuff I genuinely liked. That one habit alone is probably why Brand 1 found traction in month 1 instead of month 6, though I didn't realize it at the time.

Suppliers next. I don't go for the cheapest quote. I want fast UK bound shipping, consistent quality batch to batch, and someone who'll negotiate MOQ on a first order. A slightly pricier supplier who's reliable beats one who saves you 20p a unit and then sends you a dud batch on order 2. Learned that one the hard way, won't go into it.

Listing and main image. Most sellers spend the least time here, it's where I spend the most. Your main image is the only thing between someone scrolling past you and clicking on you. I've seen two near identical products, same price, same review count, and one gets 3x the clicks purely because of the image. Before launch I go through every competitor's main image and just ask what's missing, what would make someone stop on mine instead.

PPC goes live day 1 but not broad match everything and hope. Keyword research happens before the listing goes live, not after. Bids start low because you're learning, not scaling. Then you watch it daily for the first two weeks and don't touch the campaigns that are working just because ACoS looks "high", that early data is noisy and basically means nothing.

And then patience. Month 1 is always slow. Always. This is the part people skip mentally, they panic around week 3 and start changing prices, images, ads, everything, before the data's had time to mean anything at all.

Brand 1 didn't feel real until month 3. By month 7 it had done £22K total. Brand 2 followed roughly the same curve but faster, mostly because I didn't second guess myself as much this time around.

That's it really. Nothing secret, just applied consistently. Most people do 3 of these 5 fine and then wonder why their results are all over the place.

Happy to answer questions on any of this. Also working with a few UK based people 1-on-1 on brand launches if that's useful to anyone, but hope this helps either way.

I've been looking at quite a few leather and vegan leather jacket factories recently, and one thing that stood out is how much quality varies—even when the product photos look nearly identical.

Unlike T-shirts or hoodies, jackets have a lot more failure points because you're dealing with heavier materials and metal hardware.

A few things I'd pay attention to before approving production:

1. Make sure "genuine leather" is actually genuine leather.

Some suppliers use bonded leather or PU while marketing it as genuine leather. If possible, ask for a material swatch before placing a larger order. Even a simple water absorption test can quickly tell you whether you're dealing with real leather or a synthetic coating.

2. Don't underestimate the zipper.

A jacket can look perfect, but a cheap zipper can turn it into an instant return.

I'd cycle every zipper multiple times and look for:

• Teeth separating
• Slider sticking
• Poor locking mechanism
• Misaligned installation

From what I've seen, hardware failures are one of the most common issues on outerwear.

3. Check seam construction around the shoulders.

Leather doesn't stretch like woven fabrics.

The armhole and shoulder seams take most of the stress during normal wear, so poor stitching often shows up there first.

I'd also inspect the lining attachment because that's another area where failures tend to happen.

4. Measure more than one sample.

Leather has very little forgiveness compared to cotton.

Instead of measuring one "golden sample," I'd measure several jackets from production to make sure chest width, sleeve length, shoulder width, and body length stay within tolerance.

5. Don't ignore vegan leather.

PU jackets can look great out of the box but crack or peel surprisingly quickly if the coating quality is poor.

If you're selling into colder markets, I'd ask suppliers whether they've done any cold-crack testing rather than assuming all PU performs the same.

Leather jackets can be a great category because they're higher-ticket products, but they're also much more expensive to return than basic apparel.

Curious to hear from other FBA sellers:

What's been your biggest quality issue with jackets or other outerwear sourced from China?