Progress Agentic RAG

Progress Agentic RAG is an enterprise agentic RAG platform that indexes documents, files, and media into a governed knowledge layer for search, assistants, and agent workflows.

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Agentic RAG for enterprise knowledge

Progress Agentic RAG is an agentic RAG platform that turns enterprise documents, files, and media into a governed knowledge layer for AI assistants, agents, and search. It combines ingestion, retrieval, model selection, and validation in one system so teams can build AI experiences from the same indexed content source.

The platform is positioned for enterprise AI use cases where content needs to be searchable, cited, access-controlled, and reusable across multiple experiences. The source describes support for semantic search, source citations, audit logs, role-based access controls, and a range of plan tiers from a free trial to custom enterprise pricing.

Core capabilities

Multi-format ingestion

The platform ingests 30+ file formats, including documents, spreadsheets, audio, video, and more, into one indexed foundation that stays continuously updated.

Retrieval tuning

It supports hybrid search, chunking, and metadata filtering so retrieval can be tuned per use case without changing the underlying infrastructure.

Model selection and switching

The system lets teams choose from 40+ supported LLMs and swap models as requirements change without re-indexing the knowledge layer.

Validation and traceability

Answers are backed by source citations, audit logging, and built-in REMi evaluation metrics so teams can review and improve output quality.

Security and access control

Enterprise controls include permission-aware retrieval, role-based access controls, and end-to-end audit logging.

Expanded AI tasks on higher tiers

The pricing page lists AI classification, generative search, AI Assistant, and document summarization on Pro and Enterprise plans.

Common ways teams use it

Internal knowledge hubs
Centralize internal documents and media into a single governed layer so employees can ask questions and retrieve grounded answers from approved content.
Search experiences
Build AI-powered search experiences for portals, intranets, or customer-facing properties where results need to be relevant and source-backed.
AI assistants
Deploy assistants that answer questions across the enterprise knowledge base while respecting access controls and audit requirements.
Engineering and documentation
Support technical teams by indexing specs, documentation, and institutional knowledge across different file formats and sources.
Customer support self-service
Improve support workflows with grounded answers drawn from approved knowledge instead of relying only on free-form model responses.

Pros and Cons

Pros

Supports a wide range of content types, including documents, spreadsheets, audio, and video.
Provides governed retrieval with citations, audit logs, and access controls.
Lets teams tune retrieval and switch among supported LLMs without re-indexing.
Offers a trial, self-serve pricing tiers, and an enterprise path for larger deployments.

Cons

Some capabilities appear tied to higher-priced tiers, such as AI classification, generative search, AI Assistant, and document summarization.
Pricing and limits vary by plan, so teams with larger content volumes may need to review indexed-data and file-size caps carefully before starting.

FAQ

Does Progress Agentic RAG offer a trial?

The trial is free for 14 days. After the trial ends, you need a paid plan to continue using the platform; otherwise the trial account is disabled after 90 days.

How is Progress Agentic RAG priced?

The platform is offered on a subscription and consumption basis. The pricing page says token usage is charged at $0.008 per token, with the first 10,000 tokens per month included on some plans.

Can subscriptions be changed after purchase?

Yes. The pricing page says users can cancel, upgrade, or downgrade from the dashboard or by contacting the company. Upgrades take effect immediately after payment, while downgrades apply at the end of the billing period.

What does the platform do after content is indexed?

The homepage says the platform indexes enterprise content into a governed knowledge layer, then uses semantic search and an LLM to produce grounded answers. The pricing page also lists RAG, Q&A, AI classification, generative search, AI Assistant, and doc summarization on higher plans.

What types of teams is it built for?

The source describes enterprise knowledge management, AI-powered search, AI assistants, engineering and technical documentation, and customer support or self-service as common use cases.