Reddit startup idea
RAG Confidence Firewall
A local-first middleware library + desktop dev console that sits between your retriever (FAISS/SQLite/Chroma) and your generator (Ollama/llama.cpp) to score retrieval quality, enforce abstention policies (“I don’t know”), and trigger smarter re-retrieval on frustration signals. It ships with eval harnesses to prevent regressions (e.g., low-similarity matches, empty-context cases) and provides explainable retrieval traces so teams can tune thresholds safely. Designed to run fully offline for privacy-sensitive and edge deployments.
- Subreddit: localllm
- Industry: AI & Machine Learning
- Target date: 2026-03-19
- Upvotes: 39
- Comments: 13
Suggested product
RAG Confidence Firewall
A local-first middleware library + desktop dev console that sits between your retriever (FAISS/SQLite/Chroma) and your generator (Ollama/llama.cpp) to score retrieval quality, enforce abstention policies (“I don’t know”), and trigger smarter re-retrieval on frustration signals. It ships with eval harnesses to prevent regressions (e.g., low-similarity matches, empty-context cases) and provides explainable retrieval traces so teams can tune thresholds safely. Designed to run fully offline for privacy-sensitive and edge deployments.
Target customer
Developers and small teams shipping local/offline LLM apps (desktop apps, on-device enterprise tools, air-gapped deployments) that use vector search + local inference.
Problem-solution fit
They need a reliable way to stop garbage-in retrieval from becoming confident, harmful outputs—especially when operating offline where cloud guardrails and managed RAG observability aren’t available. The product standardizes confidence scoring, abstention, and re-query strategies, turning a brittle DIY pattern into a testable, configurable component that reduces support burden and risk.
Keywords
- local RAG
- retrieval confidence
- abstention
- FAISS
- Ollama