RAG Loom - Turn your RAG prototype into a trusted production system

RAG Loom is a production native platform that unifies model selection, vector database connectivity, automated deployment, real-time observability, and enterprise guardrails — so teams launch trustworthy AI pipelines in days, not months.

From RAG demo to reliable, scalable, compliant Gen AI system

Most teams can build a prototype — few can take it to production. RAG Loom makes that transition seamless and secure.

Deploy with Docker & Kubernetes
with enterprise guardrails

Auto-generated blueprints, real-time observability, and built-in PII protection meet GDPR and EU AI Act standards — reducing risk and cost from day one.

RAG Loom - Key Capabilities

End-to-End RAG Platform

Design, test, and deploy full RAG pipelines in one seamless workflow — no glue code or tool chaos.

Plug-and-Play LLM Switching

Switch between GPTs, Claude, Mistral, and Ollama instantly without pipeline rewrites.

Smart Vector Connectivity

Pre-built integrations with pgvector and top vector stores cut setup time and keep embeddings consistent.

Auto-Deploy Anywhere

One-click Docker & Kubernetes blueprints for cloud, hybrid, or on-prem environments.

Live Observability & Cost Control

Track latency, cost, and pipeline health live to ensure predictable performance at scale.

Enterprise-Grade & Compliance

PII protection, audit trails, and full GDPR / EU AI Act alignment to meet regulatory standards.

Launch production-ready RAG pipelines faster

About

Our Mission

At Synapse Flux Lab, we build the infrastructure that makes Generative AI trustworthy, auditable, and compliant by design. Our flagship product, RAG Loom, empowers organizations to transform RAG prototypes into secure, reliable, and production-ready AI systems. By unifying orchestration, model and vector connectivity, automated deployment, and real-time observability within a single platform, we help teams launch trustworthy AI in days rather than months — fully aligned with GDPR and the upcoming EU AI Act.

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