Launch HN: Channel3 (YC S25) – A database of every product on the internet
25 comments
·August 20, 2025seanw265
I assume that payments from purchases come from you guys, rather than me needing to create and manage an affiliate account with each individual vendor?
You say that commissions average 5%, but what is the variability and where does it come from?
Last, a bit of feedback about the product.
I tried searching "nintendo switch 2" on your homepage and the results that came up kind of sketched me out. You mention that the products are US-only, but the first result clearly says "hong kong" in the title. And the store listed is "My Nintendo Store PT"; is that the official store? When I google that it takes me to the Portuguese version of the nintendo website, and that makes me even more confused.
The second result for the same search appears to be a dress, which is obviously completely unrelated to video games in general.
EDIT: I'm noticing irrelevant results for many queries. Searching "plain white pillowcase", the third result is a t-shirt, the seventh result is a dress, and the eleventh result is a light bulb.
Searching "men's wallet" the very first result is an outdoor picnic table.
glawrence13
Regarding payments, your understanding is correct. We have and manage our affiliate partnerships, all you have to do is drive sales and we forward on the commission to you. We're working on improving signal into the range of commissions you can expect, but, in short, the variability stems from merchants and product type. For example, technology (e.g. iPhones, laptops) typically have lower commissions than beauty supplies.
Thanks for the feedback. Managing and cleaning this volume of data is an ongoing task, and our catalog is getting better each day. I'll check out the nintendo case in particular.
seanw265
Yes certainly! I've dealt with large datasets like this in the past and know firsthand how challenging it can be to wrangle them.
Something like this would be a great fit for my travel planner app if I knew I could trust that the results were high quality before prompting the user with them.
Btw I edited my earlier comment with a few more examples just before you replied.
Good luck!
glawrence13
Appreciate it. FYI, for the specific bugs you flagged, looks like Nintendo was improperly named (reindexing products with that name now), and sounds like the pain point you felt was extraneous search results that really didn't belong. Transparently, the problem we're facing there is vector search can be a bit of a black box, so we're trying to tune our hybrid search to cull out really crazy results, but obviously it still needs work.
One of the ways we're combatting these search problems in the early days is developers can curate their catalog with specific brands, merchants, and categories (and even down to the product level) so you know exactly what the search space for each of your queries is. Curious to hear about your travel planner app -- if you think this would be a helpful tool, feel free to reach out at george@trychannel3.com
joloooo
One of our clients is a procurement marketplace. One of the current struggles we face is getting vendors to upload catalog details for each product, which our marketplace needs to populate for a better shopping experience. Would your API assist us with filling in those gaps on products? (think common office products, industrial equipment etc). The caveat is that we can only show products from specific compliant vendors.
AmazingTurtle
If a quick POC is useful, we (everfind.ai) can help fill product-data gaps while honoring your "compliant vendors only" rule. We ingest whatever you have (text, JSON, HTML, Excel), map it into category-specific schemas, and surface clean fields for search/filter. You also get fast fielded search, sensible relevance, hybrid vector search, and an optional guided-selling assistant you can expose publicly. Happy to try this on a small sample.
Feel free to reach out to me at felix.faust@everfind.ai :)
glawrence13
Certainly! Feel free to reach out to me directly at george@trychannel3.com with specific vendors.
CharlesW
How do you plan to differentiate vs. prompting a foundation model provider (interactively or via API for an affiliate site) with, "show me outdoor grills, between 500 and 1000, from weber"? https://imgur.com/a/Vdw4E1S
glawrence13
Certainly, for end users, foundation model providers (just ChatGPT right now) will be a great option for shopping. But, we don't believe it'll be the only place people shop. With Channel3, developers can build their own agentic shopping experience, and they also can monetize it.
CharlesW
By "via API for an affiliate site", I was referring to developer uses cases where the result can be used in a traditional website, by an AI agent, as a "related products" mechanism in an already existing system, etc. in order to monetize it. In that case, what will differentiate you?
glawrence13
Ah, sorry for misunderstanding. There are a bunch of challenges with that approach that my co-founder experienced first-hand (actually, that exact workflow is why we started Channel3!). To get up-to-date info, you'd need realtime websearch -- that's slow and expensive. To monetize, you'd need to set up affiliate relationships yourself. And, at the end of the day, the info you get from foundation models isn't really sufficient for building a rich shopping experience. So, someone could try that approach, or they could just use our API for cheaper and not deal with the hassle.
psawaya
Nice! Reminds me of an old school YC co called Semantics3. Any connection?
glawrence13
haha, no, hadn't heard of them until now. uncanny resemblance though!
AmazingTurtle
Searching for Ryzen 9950X3D yields 6 different AMD processors before I see a fully built PC with that Ryzen 9950X3D included. The actual product I was looking for was ranked 16th.
glawrence13
Thanks for the feedback! We're working on improving search, and it's helpful to hear the different ways it's currently coming up short.
GistNoesis
I think there is something wrong with your face filter settings on all your videos.
peapod91
Are Amazon products available in your database?
glawrence13
Not yet, we're working on it though
SeanSullivan86
https://www.gs1.org/services/gdsn/global-data-model
As someone who has worked in e-retail, this catalog seems to have a lot of momentum.
glawrence13
Thanks for sharing. Always a fan of standardization of information online.
mfrye0
Hey George and Alex. This looks awesome. We're working on something similar, but for all of the businesses in the world: https://savvyiq.ai. We're international and have 265M+ entities in the system. We're actually preparing to do our own formal share on HN shortly.
We're working with enterprise customers now that want to use our system to dedupe all their gnarly business data, ground it to real legal entities, enrich it with base insights, then are asking for further data points more from a risk and due diligence standpoint.
Product information has come up repeatedly, but as you clearly know, that is a beast in itself that I don't think we'll ever tackle. For context, I helped build out the product data infra at https://www.wiser.com, and I'm not inclined to spend my time categorizing and building the taxonomy for pots, pans, and towels again.
I'm going to try out the product and happy to chat further if you think there's an opp to collaborate in some way. My email is in my profile.
glawrence13
Thanks for the comment. Will shoot you an email!
Hi HN — we’re George and Alex, building Channel3 (https://trychannel3.com/), a database of every product on the internet, searchable via text/image, and with built-in affiliate monetization. Here’s a demo: https://www.youtube.com/watch?v=Mx8FyP7KvJg.
It’s surprisingly hard to find good product data. If you want your software to recommend products and deep-link to merchants, you’ll quickly discover that the data you need—clean titles, normalized attributes, deduped listings, current prices and inventory, variant options, images, and brand info—is not just messy; it’s also spread across a long, long tail of retailers, and often lives behind advanced bot-detection systems.
We ran into this problem while building an AI teacher that could recommend relevant supplies. We asked Exa for products, but got back articles, not structured data. Same for Tavily and Bing (deprecated as of 8/13/25). And we got rejected from affiliate programs, who suggested we come back with 1000s of TikTok followers. Channel3 is the API we wished we had.
Product detail pages (PDPs) usually present the main item alongside recommendations. We use computer vision to isolate the main product and find its attributes, like title and price. We apply the same logic to the rest of the PDPs on the domain.
Products are often sold across multiple retailers, with no guarantee they’ll be labeled consistently. We collapse products across the web into a canonicalized set by using LLMs and multimodal embeddings to actually understand each product.
To normalize everything into a schema that tries to be both minimal and extensible, we have to be opinionated. Are a $50 10” and $60 12” skillet the same product? Probably not, but the S/M/L variants of a T-shirt are. Our goal is that any product you’d search for specifically is treated as its own product.
We process a massive amount of data. We quickly ran out of room on our Cloudflare Vectorize indices and moved to the brand-new AWS S3 Vectors platform, syncing to OpenSearch for faster response times and more dynamic filtering. We hit rate limits constantly, so we spread our work over multiple cloud providers and AI models.
You can search things like “outdoor grill, less than $1000”, or “sweat-resistant, wireless running earbuds”, or "women's jeans from Paige that look like [https://www.gap.com/webcontent/0020/666/799/cn20666799.jpg]”. Products come back as JSON with title, brand, images, price, specs, etc.
Developers earn commission on sales they drive (averaging 5%). Channel3 takes a cut. We want you to earn way more money from Channel3 than you spend on it. We win when you win.
We provide an API, SDK (Typescript and Python), and MCP. We offer 1000 free searches, and charge $7/1000 searches after that. You can view expected commissions per-brand on our dashboard.
So far, products are US-only (sorry! we will expand). We’re live with millions of products and hundreds of developers.
To get started, make a free account at https://trychannel3.com, then select which brands you’d like to sell (choose from 50k+ or request your own), generate an API key, and start selling and earning.
We’d really appreciate feedback from this community. If you’ve built product search before, what did we miss in the schema? If you’ve tried to add commerce to an app, what blocked you? If you tried to build this yourself, what did you learn? Are there endpoints you wish existed (e.g. price alerts, back-in-stock webhooks, product feed)? For any suggestions, we’re all ears.
We’ll be in the thread all day to answer questions, share more technical detail, and hear whatever would make this most useful to you. Comment away!