rag_control
Early signalRuntime governance and policy controls for RAG systems
Launched 1mo ago
Signal
38
Velocity
0
What this means
1.6×Growing 1.6× faster than the typical Developer Infrastructure launch.
Compared to 105 Developer Infrastructure launches at the same age.
Decaying
Vote pace down to 0% of launch speed.
20Founder Conviction Index: 20 — low signal.
Few of the conviction sub-signals (reputation, velocity, buyer-intent, tagline clarity) are firing yet.
Prediction
Top-5 finish probability
5%
today
Projected end-of-day votes
12range 9–16
Trajectory
decaying
Vote pace down to 0% of launch speed.
Speed vs peers
1.6×
105 Developer Infrastructure launches
About
A Python control layer for governing RAG applications at runtime. Define generation policies, filter retrieval by document metadata and user context, apply organization-level rules, and inspect decisions with audit logs, traces, and metrics. Works with your existing LLM and vector-store adapters. Built by RetrievalLabs, which is making enterprise software development end-to-end AI-native.
AI Summary
rag_control is a Python control layer designed for runtime governance of RAG applications, allowing users to define policies, filter retrievals, and apply organization-level rules. It integrates with existing LLM and vector-store adapters and provides audit logs, traces, and metrics for decision inspection.
Attention & discussion velocity
Performance
Velocity0
Vote pace vs average
Momentum0
Sustained over 6h
Virality0
Spread × engagement
Engagement50
Discussion per unit of attention
Signal sources
2 of 6 active- Launch tracking10 readings since we picked it up
- AI analysisDeveloper Infrastructure
- Comment intelligencethread not yet analysed
- Outside discussionnot checked — domain is a storefront or shared
- Revenue signalsno match on this domain
- Editorial scoringbelow the signal threshold for this pass
A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.