Agentic RAG Platform
Agentic RAG pipelines for production enterprise AI
Build agentic RAG systems that plan retrieval, search across governed knowledge sources, call tools, synthesize answers, enforce access controls, and continuously evaluate retrieval quality in production.
Fast answer
Inwire.ai builds production RAG pipelines and agentic RAG workflows that ingest enterprise data, create RAG embeddings, use hybrid search and reranking, preserve access controls, call tools, cite sources, and monitor retrieval quality.
Production outcomes
Connect agents to documents, databases, APIs, tools, and governed enterprise knowledge.
Use hybrid search, reranking, chunking strategies, metadata filters, and access-aware retrieval.
Monitor retrieval quality, citations, latency, failures, and freshness over time.
RAG that can plan and act
Agentic RAG lets an AI workflow decide what to retrieve, when to call a tool, how to combine sources, and when to ask for more context instead of forcing every query through a static retrieval pattern.
Enterprise-grade retrieval foundations
Inwire.ai supports ingestion, chunking, RAG embeddings, vector search, sparse search, hybrid retrieval, reranking, metadata filters, permissions, freshness tracking, and evaluation loops.
Production controls for AI agents
Deploy agentic RAG with endpoint security, observability, guardrails, traceability, model routing, and controlled access to enterprise tools and knowledge bases.
What inwire.ai can run and optimize
Ingest documents, wikis, PDFs, databases, APIs, tickets, code, and governed knowledge bases.
Create RAG embeddings with chunking, metadata enrichment, vector search, sparse search, hybrid retrieval, and reranking.
Let agents plan retrieval steps, call tools, compare sources, synthesize answers, and cite the evidence used.
Monitor retrieval relevance, hallucination risk, citation quality, freshness, permissions, latency, and failure modes.
Questions teams ask before rollout
What is agentic RAG?
Agentic RAG is retrieval-augmented generation where an AI agent can plan searches, select tools, retrieve from multiple sources, and synthesize answers with more control than a static RAG chain.
Does Inwire.ai support enterprise access control for RAG?
Yes. RAG workflows can preserve permissions, apply metadata filters, enforce access controls, and log retrieval decisions for auditability.