Progress Agentic RAG

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Progress Agentic RAG sets a new standard for enterprise Generative AI adoption. The nomination highlights measurable achievements, including launching a modular, multi-modal RAG-as-a-Service platform that ingests over 60 data formats across multiple languages.

Its innovations—including low/no-code deployment, 30+ retrieval strategies, provenance tracking, and RAG Evaluation Metrics—address the most pressing enterprise challenges: fragmented data, technical complexity, and trust in AI outputs. Real-world deployments in legal research, customer support, and finance illustrate tangible improvements in transparency, time-to-value, and operational efficiency.

Overall, Progress Agentic RAG exemplifies how enterprise AI can be trustworthy, scalable, and impactful, enabling organizations to unlock insights from previously inaccessible unstructured data. - Progress Agentic RAG is a highly compelling nominee in agentic AI and enterprise Generative AI. The submission effectively demonstrates innovation, scalability, and real-world impact, providing a clear blueprint for trustworthy, governed AI adoption at scale.****
The submission showcases a strong technical foundation and clearly addresses data and deployment complexity. Its modular design makes it a great choice for GenAI adoption. - The submission has strong technical foundation but lacks data-driven outcomes. The submission should include metrics like cost savings, accuracy and business impact to make the submission more stronger.****
Solid concept with a clear enterprise use case. This submission explains the idea behind the platform clearly and addresses a real challenge around making enterprise GenAI usable across large datasets. I also liked the approach to simplifying RAG implementations for enterprises. - A good approach to helping organizations turn complex data into useful insights with AI****
Unlocking "Dark Data": Focus on their ability to turn unstructured, multimodal data (audio, video, etc.) into a queryable enterprise asset. This is a major leap for data democratization.

The Governance Advantage: Highlight that their proprietary evaluation framework (REMi) and full audit trails provide the "enterprise-grade" trust that traditional RAG stacks often lack.

Modular Accessibility: Commend the no-code, LLM-agnostic approach. It allows organizations to remain agile and avoid vendor lock-in while providing a "memory layer" for their AI agents. - GraphRAG Granularity: Given your work on projects like VeritasGraph, you might ask for more detail on how their "inference agents" determine relationship weights in the knowledge graph. Is this purely semantic, or is there a human-validated ontological layer?

Evaluation Depth: The REMi (RAG Evaluation Metrics) sounds promising. You could ask how it handles "hallucination detection" in multimodal sources, specifically for audio and video where context windowing is more complex.

Data Latency: Ask about the performance overhead of the 60+ format ingestion pipeline. How do they maintain "time-to-value in days" when processing massive, heterogeneous data lakes?****
Would like to refrain from providing public feedback. - Need quantified outcomes(from testimonials) that show business impact beyond platform capability and this is vital for new customer onboarding.****
Progress Agentic RAG presents an interesting approach to simplifying enterprise adoption of generative AI by addressing challenges around fragmented data, transparency, and technical complexity. The platform’s modular architecture and focus on traceability and governance are particularly relevant as organizations look for more reliable ways to operationalize AI across business workflows. - The submission outlines a thoughtful approach to addressing common barriers in enterprise GenAI adoption, particularly around data fragmentation and trust in AI-generated outputs. The emphasis on modular architecture, auditability, and LLM-agnostic design is valuable for organizations seeking flexibility and governance. To strengthen the nomination further, additional real-world deployment metrics or quantified outcomes from customer implementations would help better demonstrate the platform’s measurable business impact.

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A strong and practical submission that clearly solves a real enterprise AI problem and makes GenAI easier to use and trust.
Progress Agentic RAG is a highly compelling nominee in agentic AI and enterprise Generative AI. The submission effectively demonstrates innovation, scalability, and real-world impact, providing a clear blueprint for trustworthy, governed AI adoption at scale.
I get the idea behind the innovation and how it can apply across industries, however I would be interested in knowing the retrieval strategy innovation. I would also like to see some measurable enterprise gains.
The submission has strong technical foundation but lacks data-driven outcomes. The submission should include metrics like cost savings, accuracy and business impact to make the submission more stronger.
A good approach to helping organizations turn complex data into useful insights with AI
GraphRAG Granularity: Given your work on projects like VeritasGraph, you might ask for more detail on how their "inference agents" determine relationship weights in the knowledge graph. Is this purely semantic, or is there a human-validated ontological layer?

Evaluation Depth: The REMi (RAG Evaluation Metrics) sounds promising. You could ask how it handles "hallucination detection" in multimodal sources, specifically for audio and video where context windowing is more complex.

Data Latency: Ask about the performance overhead of the 60+ format ingestion pipeline. How do they maintain "time-to-value in days" when processing massive, heterogeneous data lakes?
Need quantified outcomes(from testimonials) that show business impact beyond platform capability and this is vital for new customer onboarding.
The submission outlines a thoughtful approach to addressing common barriers in enterprise GenAI adoption, particularly around data fragmentation and trust in AI-generated outputs. The emphasis on modular architecture, auditability, and LLM-agnostic design is valuable for organizations seeking flexibility and governance. To strengthen the nomination further, additional real-world deployment metrics or quantified outcomes from customer implementations would help better demonstrate the platform’s measurable business impact.
Progress Agentic RAG presents a remarkably tactical offering for 2026. Their approach "SaaS-ifies" sophisticated Retrieval-Augmented Generation to address two significant challenges: the dreaded "Infrastructure Tax" and the pervasive issue of data cynicism. By incorporating a patented REMi evaluation framework and supporting multimodal ingestion across 60+ data formats, Progress delivers a platform that not only bridges the gap between siloed unstructured data and trustworthy AI but does so with no-code verifiability. The outcomes speak for themselves, boasting an impressive 80% reduction in deployment costs alongside a flawless 5.0 Forrester Wave score in Search. But it's the realistic applications that truly set this platform apart: it has truly escaped pilot purgatory and entered the realm of governed, production-ready deployment. This is top-of-the-pack stuff. With Progress’ solution, high-trust, agentic research is not just possible – it's scalable, dramatically shifting the "buy vs. build" paradigm.

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