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FEVER Multimodal DB
Self organizing business media files, once and for all.
Does your app allow users to upload photos, video, audio clips, or documents? Have you implemented natural language search for them? Do you know what those users are actually uploading? Would it make your team go faster experimenting with AI if there was a way to curate training data by content? FEVER Multimodal DB answers for all that, at max speed. It comes with our bespoke AI baked in, fully private in your walled private compute cloud - we never see your user data. No GPU needed either!
(P.S. Upfront - we are on AWS 3rd Party marketplace for our initial launch, for easy and fast procurement, standard contracts, and easy deployment and integration. Email us at [email protected]; or book a demo on our site and I will issue you a private offer for a low cost trial that will convert to paid, fully prorated!)
I was inspired to build this after years of wrangling media data as a Machine Learning Engineer, constantly frustrated by what we still couldn’t easily do in the "age of AI" because handling massive volumes of unstructured data remains so painful.
Businesses have tons of data, but engineers are stuck evaluating models, backfilling and indexing datasets, figuring out what "BM25" even means, and stitching together complex search systems just to make assets discoverable, compliant, and usable.
I envisioned a "warehouse where every box automatically finds its own cubby hole." To make files truly self-organizing, I founded an AI research lab to build an ultra-efficient, physics-informed model architecture called FEVER.
This patent-pending approach is why our engine can rip through millions of photos per day on a single server. It runs with or without a GPU; meaning your team doesn't have to fight for scarce hardware. It never phones home over the network. Your data stays entirely in your environment: zero leakage, maximum privacy, and low latency.
Our core thesis is that AI is only valuable when it respects existing developer workflows. We don’t reinvent the database: I spent years as a backend developer, after all. Instead, we bake AI directly into the tools you already rely on and make them significantly smarter.
Apply FEVER to organize your media files, deploy production-ready natural language search in minutes, and skip the hefty bills from managed vector databases with 3rd party AI; or proprietary search platforms.
Power entirely new product experiences. Imagine:
Real Estate: Searching for a sunny villa visually and finding listings by "vibes."
Insurance: Enabling adjusters to automatically compare video damage profiles, saving consumer dollars!
E-Commerce: Delivering intelligent visual substitutes instantly when an exact item is out of stock.
User Content: Auto-tagging customer uploads so users never have to manually categorize files again.
Beyond search and fingerprinting, FEVER helps you curate internal training datasets and strengthen trust, safety, and compliance by instantly surfacing high-risk uploads hidden across your storage buckets. Use it to curate AI training data at scale too, make your own systems better with cleaner, more relevant data!
This is media intelligence. This is the future of self-organizing files. Let us handle the heavy lifting! 🙂
About FEVER Multimodal DB on Product Hunt
“Self organizing business media files, once and for all.”
FEVER Multimodal DB was submitted on Product Hunt and earned 11 upvotes and 2 comments, placing #32 on the daily leaderboard. Does your app allow users to upload photos, video, audio clips, or documents? Have you implemented natural language search for them? Do you know what those users are actually uploading? Would it make your team go faster experimenting with AI if there was a way to curate training data by content? FEVER Multimodal DB answers for all that, at max speed. It comes with our bespoke AI baked in, fully private in your walled private compute cloud - we never see your user data. No GPU needed either!
On the analytics side, FEVER Multimodal DB competes within Developer Tools, Artificial Intelligence and Database — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how FEVER Multimodal DB performed against the three products that launched closest to it on the same day.
Who hunted FEVER Multimodal DB?
FEVER Multimodal DB was hunted by Suraj Mirpuri. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of FEVER Multimodal DB including community comment highlights and product details, visit the product overview.
Suraj Mirpuri
(P.S. Upfront - we are on AWS 3rd Party marketplace for our initial launch, for easy and fast procurement, standard contracts, and easy deployment and integration. Email us at [email protected]; or book a demo on our site and I will issue you a private offer for a low cost trial that will convert to paid, fully prorated!)
I was inspired to build this after years of wrangling media data as a Machine Learning Engineer, constantly frustrated by what we still couldn’t easily do in the "age of AI" because handling massive volumes of unstructured data remains so painful.
Businesses have tons of data, but engineers are stuck evaluating models, backfilling and indexing datasets, figuring out what "BM25" even means, and stitching together complex search systems just to make assets discoverable, compliant, and usable.
I envisioned a "warehouse where every box automatically finds its own cubby hole." To make files truly self-organizing, I founded an AI research lab to build an ultra-efficient, physics-informed model architecture called FEVER.
This patent-pending approach is why our engine can rip through millions of photos per day on a single server. It runs with or without a GPU; meaning your team doesn't have to fight for scarce hardware. It never phones home over the network. Your data stays entirely in your environment: zero leakage, maximum privacy, and low latency.
Our core thesis is that AI is only valuable when it respects existing developer workflows. We don’t reinvent the database: I spent years as a backend developer, after all. Instead, we bake AI directly into the tools you already rely on and make them significantly smarter.
Apply FEVER to organize your media files, deploy production-ready natural language search in minutes, and skip the hefty bills from managed vector databases with 3rd party AI; or proprietary search platforms.
Power entirely new product experiences. Imagine:
Real Estate: Searching for a sunny villa visually and finding listings by "vibes."
Insurance: Enabling adjusters to automatically compare video damage profiles, saving consumer dollars!
E-Commerce: Delivering intelligent visual substitutes instantly when an exact item is out of stock.
User Content: Auto-tagging customer uploads so users never have to manually categorize files again.
Beyond search and fingerprinting, FEVER helps you curate internal training datasets and strengthen trust, safety, and compliance by instantly surfacing high-risk uploads hidden across your storage buckets. Use it to curate AI training data at scale too, make your own systems better with cleaner, more relevant data!
This is media intelligence. This is the future of self-organizing files. Let us handle the heavy lifting! 🙂