Mnemcore turns your organization’s video into searchable, shared knowledge. Capture timestamped notes, search across every recording, and ask questions to get grounded answers linked directly to the original moments. Built for teams that rely on video but need a better way to remember, find, and act on what they see.
Hey Product Hunt! I’m Jose, the founder of Mnemcore 👋
I built Mnemcore after spending years between two worlds: software engineering and competitive rugby.
Teams record countless hours of video and capture valuable observations, but that knowledge usually stays scattered across individual recordings, notes, and people’s memories.
Mnemcore turns those timestamped observations into searchable organizational memory.
Teams can:
• Capture notes connected to exact moments
• Search across every video at once
• Ask questions in natural language
• Get evidence-backed answers linked to the original timestamps
What makes Mnemcore different is that it does not try to replace the coach, analyst, or expert. Their observations are the knowledge base. AI helps preserve, connect, and retrieve what the organization has already learned.
We’re starting with sports, but we believe this applies anywhere teams learn through video: training, education, research, operations, interviews, and creative work.
I’d especially love feedback on the idea of organizational memory, the current workflow, and which industries beyond sports could benefit most.
Thanks for checking out Mnemcore!
the rugby-to-general-teams jump is the part I'd want to hear more about. match footage is short, physical, and the "moment" is usually obvious - a tackle, a missed pass. a lot of the other use cases you listed, like research interviews or ops reviews, are long and verbal, where the important moment is a sentence buried in an hour of talking, not a visual event. is the retrieval doing anything sport-specific under the hood right now, or is the current model already general enough that a coach's clip and a two-hour interview get indexed the same way
Congratulations on the launch. The timestamped evidence is what makes this interesting for me. A lot of teams collect hours of video, but the useful insight still lives in someone’s notebook or memory. I could see this working well beyond sport in areas like healthcare training, manufacturing reviews, user research and field operations. One thing I’d like to understand is how Mnemcore handles disagreement. If two coaches or analysts interpret the same moment differently, does it preserve both views and show who added each observation?
the grounded-answer-linked-to-the-original-moment part is what separates this from just running transcripts through a search index, most tools give you a text blob back and you still have to hunt for where in the recording it actually happened. does it handle screen shares and slides shown during the call too, or mainly spoken content?
Congrats on the launch. The answer layer is the part I got curious about: when someone asks a question and gets a citation back, is retrieval scoped to the videos that person can already open? Answering across a whole workspace seems like a fun problem to get right.
About Mnemcore on Product Hunt
“Turns hours of team video and notes into searchable memory”
Mnemcore launched on Product Hunt on July 23rd, 2026 and earned 70 upvotes and 5 comments, placing #39 on the daily leaderboard. Mnemcore turns your organization’s video into searchable, shared knowledge. Capture timestamped notes, search across every recording, and ask questions to get grounded answers linked directly to the original moments. Built for teams that rely on video but need a better way to remember, find, and act on what they see.
Mnemcore was featured in Artificial Intelligence (474.3k followers), Search (18.1k followers), Online Learning (3.6k followers) and OpenAI Day (8 followers) on Product Hunt. Together, these topics include over 117.3k products, making this a competitive space to launch in.
Who hunted Mnemcore?
Mnemcore was hunted by Jose. 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.
Want to see how Mnemcore stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Jose