Unabyss
MCP-native self-updating context layer for your AI
2.4K followers
MCP-native self-updating context layer for your AI
2.4K followers
Set it up once and never re-explain yourself to AI again. Connect the apps you use daily - Unabyss will extract, structure, and update your context automatically. Share it with any AI tool via MCP, with granular control over what each tool can see.
Products used by Unabyss
Explore the tech stack and tools that power Unabyss. See what products Unabyss uses for development, design, marketing, analytics, and more.
Engineering & Development 4
Engineering & Development 4
NeonServerless Postgres: Now Generally Available
5.0 (84 reviews)
Unabyss is Postgres-first - context docs, versioning, and vector search all run on Postgres with pgvector - so Postgres was never in question. What we wanted was managed, serverless Postgres that didn't make a small team babysit infra, plus real operational visibility (Neon surfaces slow-query insights right through MCP). It let us keep the database we'd already committed to and stop thinking about it.
AI Agents 1
AI Agents 1
ElevenLabsCreate natural AI voices instantly in any language
4.9 (201 reviews)
We use ElevenLabs Scribe for one thing - speech-to-text inside our context chat - and it's excellent at exactly that. Users talk, and Scribe turns it into clean, accurate text that flows straight into their context. We didn't need it to do everything; we needed transcription that just works, and it does. Easy to recommend if voice input is a real part of your product.
Productivity 1
Productivity 1
Finance 1
Finance 1
LLMs 1
LLMs 1
Claude by AnthropicA family of foundational AI models
5.0 (968 reviews)
Claude's Opus and Sonnet are our primary models for agentic query - the part of Unabyss that reasons and synthesizes across a user's entire context. That's the hardest thing we ask a model to do, and Claude handles long-context reasoning and multi-step synthesis better than the alternatives we've run it against. Being multi-provider, we don't have to use Claude here - we choose to, because the output quality on this specific job is worth it.