Progress Agentic RAG is an enterprise‑grade RAG‑as‑a‑Service platform that transforms documents, videos, and data into trusted, verifiable AI answers. It combines retrieval, reasoning, and orchestration with source‑level citations, built‑in quality metrics, and LLM flexibility. Deploy AI‑powered search, assistants, and workflows in days without complex pipelines or vendor lock‑in.

Hi Product Hunt! Today, we’re excited to launch the Progress Agentic RAG solution-a modular, enterprise-ready platform for building AI experiences grounded in your organisation’s knowledge. Generative AI (GenAI) is powerful, but most teams hit the same challenge: models don’t know your business. Most important information lives across documents, PDFs, videos, portals and internal systems, making it difficult for AI to output answers you can actually trust. Agentic retrieval-augmented generation (RAG) solves this. It connects to your enterprise content, retrieves the right context and generates verifiable answers with source citations—so users can see exactly where the information came from. Instead of returning a list of links, it delivers direct answers grounded in your own knowledge layer. With the Progress Agentic RAG solution, you can: 1. Deploy AI-powered search and assistants in days with a no/low-code set-up 2. Turn scattered documents, videos and files into trusted, searchable knowledge 3. Leverage any LLM (even bring you own!) while keeping your retrieval pipeline stable 4. Get built-in RAG evaluation metrics (REMi) to measure answer quality and groundedness 5. Receive traceable answers with source-level citations for transparency and compliance Teams are already using agentic RAG technology to power AI search, knowledge hubs, customer support assistants and domain-specific copilots across enterprise data. We’d love your feedback. Ask us about: • Production-grade RAG builds • Enterprise AI search • Agentic workflows • Scaling of AI beyond prototypes 👇 Drop your questions below - the team is here all day!

'Deployed in days' is the bold claim. What happens when source docs are contradictory - does the reasoning layer pick a winner or surface the conflict? That's where RAG either earns trust or loses it fast.

@mykola_kondratiuk When source documents conflict, our agentic reasoning layer does not silently “pick a winner.” Instead, it’s designed to detect contradictions, surface them explicitly, and show info origin so users can judge for themselves. Answers are grounded with citations back to each source, and when confidence is low or sources disagree, the system flags that ambiguity rather than hallucinating certainty .

surfacing the conflict rather than resolving it silently is the right call. most systems paper over this and produce confident wrong answers. showing the ambiguity is where the real trust comes from.

Progress Agentic RAG - Trusted AI answers with citations, deployed in days

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