This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Context-Harbor
Self-hosted knowledge layer for AI teams and agents
ContextHarbor helps teams build AI applications without rebuilding document ingestion, indexing, and retrieval infrastructure. Deploy it on infrastructure you control, organize knowledge into access-controlled projects, and search it through a Web UI, REST API, CLI, or MCP-connected coding agents. It combines semantic search with keyword matching for exact terms and supports optional AI answers, contextual enrichment, and remote embedding providers.
Hi Product Hunt 👋
I built ContextHarbor after repeatedly seeing the same knowledge infrastructure rebuilt for every AI project: document ingestion, semantic chunking, indexing, retrieval, access control, and integration with AI agents.
ContextHarbor brings these capabilities together in a self-hosted platform. Teams can deploy it with Docker, keep documents and vectors on infrastructure they control, organize knowledge into access-controlled projects, and use it through a Web UI, REST API, CLI, or remote MCP.
The search pipeline combines semantic retrieval with keyword matching, which helps it understand meaning while still finding exact terms such as error codes, class names, product terminology, and requirement identifiers.
I’m currently looking for engineering teams to pilot ContextHarbor in real-world environments. I’d especially value feedback on deployment, retrieval quality, MCP workflows, and the types of internal knowledge your teams need to make accessible to AI applications.
I’d love to hear what you’re building and what you currently use for your knowledge layer.
No comment highlights available yet. Please check back later!
About Context-Harbor on Product Hunt
“Self-hosted knowledge layer for AI teams and agents”
Context-Harbor was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #135 on the daily leaderboard. ContextHarbor helps teams build AI applications without rebuilding document ingestion, indexing, and retrieval infrastructure. Deploy it on infrastructure you control, organize knowledge into access-controlled projects, and search it through a Web UI, REST API, CLI, or MCP-connected coding agents. It combines semantic search with keyword matching for exact terms and supports optional AI answers, contextual enrichment, and remote embedding providers.
Context-Harbor was featured in Productivity (658.2k followers), API (98.5k followers) and Artificial Intelligence (475.9k followers) on Product Hunt. Together, these topics include over 277k products, making this a competitive space to launch in.
Who hunted Context-Harbor?
Context-Harbor was hunted by Pradeep Gudipati. 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 Context-Harbor stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Pradeep Gudipati