We built Fabrik to give AI apps memory, context, and intelligence — the superintelligence layer developers need
Fabrik is built as a persistent personal intelligence layer for AI applications.
Instead of every agent rebuilding user context from scratch, Fabrik:
Ingests and synthesizes personal data across tools
Maintains a continuously updated memory of the user
Serves resolved context on demand via API (in milliseconds)
Emits proactive signals when relevant events or patterns emerge
This allows agentic systems to:
Start with context already resolved
Skip expensive reconstruction loops
Move directly to reasoning and action
Start building at https://getfabrik.ai/
Love the focus on zero-trust and pattern detection here. Most of the industry is obsessed with building massive, reactive data dumps for RAG, but the real bottleneck for AI apps isn't the data size—it's execution timing. Giving developers a lightweight API to trigger agents based on real-time context switches and user behavior is exactly how we kill the static chatbot era.
The vision is universally big, what clicked for me is a much smaller, a much more painful reality. ~
As per me, there aren't any AI apps. (11 words)
Whenever a user returns, it’s almost like the app is meeting them for the very first time again. Wrapping memory in prompts and vectors and session hacks is fragile and costly.
What you’re describing feels like a livable value.
AI memory might not be the feature.
Your application wouldn't have to re-assemble user on every request anymore.
This is a really good framing for builders.
There are two things I would be curious about as a developer
* What degree of bias does the scheme for “personal intelligence layer” possess?
* What is the onboarding process for the first data sources?
* How will you stop this from becoming another vector store just with better branding?
If all this works as indicated, it’s less of a tool and more of a missing layer in the current AI stack, especially for agent-style products that fall apart without persistent context.
It seems like we are all trying to hand-roll something poorly at the moment.
Love the emphasis on serving resolved context instantly rather than rebuilding it each time, that’s a nuanced but powerful architectural move. Curious: how do you handle ambiguous or conflicting signals from multiple sources when resolving context in real time?