Personalized Product Recommendations AI answers the question "What software product should I use?

A machine learning solution that can be used at scale to make software recommendations tailored to each user. Powering the models is data on nearly 33,000 products and over 375,000 companies that use and recommend them.

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I’m super excited to share what’s been two years in the making. We built this Personalized Product Recommendations AI to help business users answer the question “What software product should I use?“ While there are sites that have data and reviews for products, we think this is the first time a machine learning solution can be used at scale to make software recommendations that are tailored to each user. Of course, this isn’t a substitute for deep research and trialing products, but we’re building this to be the first stop on the way to collecting more information from sites or communities like Siftery or Product Hunt 😺. Powering our models is data on nearly 33,000 products and over 375,000 companies that use and recommend them. What’s most exciting is that this is just the beginning: while the business tech landscape gets more crowded and complex, our recommendation engine gets better as it’s fed more data and learns about what products work for you. We’re also working on making proactive product recommendations. We have some more ideas about where to go next - would love 💖 to hear yours! For a little more info, you can check out our Medium post here: or go directly to
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@ggiaco Thanks for creating a useful product. Looks like it works best for certain roles than others. For example, I don't see all the products I use when I choose the Developer role. Also it looks like a database of products that go together and I don't see why you need ML for this usecase.
@sasajeev Thanks Sajeev! Great to see that you're finding it helpful. You should be able to add any product in our DB (33,000 strong now). Is there one we're missing? Let me know and we'll get it added. Or if you prefer you can submit it here: The ML comes into play in the Advanced Recommendations. At a high level, the model gets better on its own by reacting users marking recommendations as Useful/Not Useful.
@ggiaco good job guys, I really like how you're leveraging the core product to create other adjacent value for users, and at the same time continuing to build the core data set that's powering Siftery. Great virtuous cycle 👏🏽
@_pulkitagrawal Thanks Pulkit! That's exactly how we're thinking about it - "How can we create value first?" Any ideas for what you'd like to see (for Recos AI or more generally)?
@ggiaco Am I doing it right? I got an account with Siftery, spent some time and added 84 products that we use in our company The banner suggests I should get recommendations. Cool! I'm in, that's why I probably registered in the first place. But when I select a role, let's say it's Support and this is what I got there: I'm NOT seeing my items on the list and prompted to add products AGAIN. What's the catch? I've got 84 categorised products in my profile, why adding them again?
It's much funnier to discover new products and tools with Siftery rather than other websites 😀
OMG, I love it!!
Great! Seems really impressive. A software telling me what other software to use.
@kkkosariya I feel like soon that's all that will happen. Or maybe software will just pick what software to use and automatically start using it..
Does it work mainly for front end tools? How does it pick up my current stack? Is it only one time recommendations or on going? Can I request optimization for cost, performance, or scalability?
@prasanna_says All good questions! For our model to recommend a product, it first needs to be in our DB (we have about 33,000 now and growing) and then we must have some data on usage (i.e. which companies use or have used a product) and sentiment (i.e. do its users recommend it or not). We have more data on FE tools, but BE is also in the house. You can come back and get recommendations anytime! We're working on proactively pushing recs (set it and forget it!), but that's still upcoming and will be guided by the feedback we get. For optimization on cost, performance, and scalability - we don't address them directly, but if you go through the Advanced flow you will see that recommendations are often adjusted for company size, industry, geo, etc. (in the background we pull in this information for your company/domain from public sources). Therefore, you might get a recommendation because a particular product is more popular with companies of your size (e.g. an HR solution for the Enterprise). Indirectly, this can account for those variables.