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Neil Katz on Why Investigators Sample, and What Breaks When They Stop
Investigative teams examine a fraction of what they hold, not because a fraction is the right amount of scrutiny, but because it is all the attention available. Valantor Chief Product Officer Neil Katz expects AI to remove that limit within five years. The harder question is what a full review is worth if nobody can trace where its conclusions came from.
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Valantor launches FraudX as complex insurance investigations outgrow manual review
Four weeks after bringing EyeLevel and GroundX into the Enterprise Visual Intelligence platform, Valantor is putting that technology to work on one of the most document-heavy problems in the enterprise: investigating insurance claims.
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How a Russian Gang Used American Companies To Create a Fraud Empire
324 defendants, $14.6 billion in attempted fraud, and one Russia-linked ring responsible for more of it than all 300-plus other defendants combined. The real money in insurance fraud has never been in the opportunist filing one bad claim. In this article we describe the Fraud, and explore how AI can be used to fight it.
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AI Pilots Were Dying in 2025. In 2026 CEOs Are Taking Charge
MIT found that 95% of AI pilots failed in 2025, undone by poor workflow fit, over-customization, and building instead of buying. In 2026, AI spending is doubling and CEOs, not CIOs, are taking ownership, with half saying their job is on the line if it doesn't pay off.
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Valantor acquires EyeLevel as enterprises rethink where AI belongs
Valantor has acquired EyeLevel, the company behind GroundX, expanding its Enterprise Visual Intelligence platform as organizations move past early AI experimentation and confront a harder problem: deploying AI without exposing sensitive information.
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Seamless Partners on the foundations of Visual Intelligence for document workflows
A new analysis from Seamless Partners examines how GroundX, the technology now industrialized inside Valantor's GroundX Studio, closes the data comprehension gap for enterprises operating on visually complex documents.
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Air France / KLM Case Study
How Air France / KLM hit 96.2% accuracy on visually complex policy documents, beating their 60% target with Valantor's Visual Intelligence platform.
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AskVet Case Study
How AskVet doubled gross margins from 40% to 80% by operationalizing a decade of veterinary data with Valantor, powering VERA, the world's first digital veterinarian.
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GroundX Achieves Superhuman Performance in Document Comprehension on DocBench
Watch the full video on ourYouTube:
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How to Build a RAG System on Prem
Explore this topic in more detail with our YouTube video:
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Build Secure and Accurate RAG for Regulated Industries on OpenShift with GroundX
The world’s most important information doesn’t live on the public Internet and never will. That’s why Valantor designed their next generation RAG (retrieval augmented generation) platform to run in the most secure data centers, including air-gapped. In this use case, we’ll explain how to set up a
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How to Evaluate RAG? Follow Our 7 Simple Steps
The bad news first. There’s no easy button for evaluating RAG (retrieval augmented generation) systems. The public data sets aren’t great, preparing your own data requires subject matter expertise and the auto evaluation platforms that promise to automate much of the work, don’t really deliver, at l
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RAG Evaluation: Almost Everything You Need to Know
As developers converge around two core paradigms for LLM applications — RAG and Agents — the need to evaluate their performance has become a major hurdle to launching production ready products.
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Do Vector Databases Lose Accuracy at Scale?
Vector databases, a key technology in building retrieval augmented generation or RAG applications, has a scaling problem that few are talking about.
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World's Most Accurate RAG? Langchain/Pinecone, LlamaIndex and Valantor Duke it Out
Retrieval Augmented Generation (RAG) is a critical tool in modern large language model (LLM) powered applications. But like LLMs themselves, RAG systems are prone to hallucination - making up incorrect information in response to user questions. Accuracy is now a critical measure of RAG and LLM perfo
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Optimizing RAG Systems with Advanced LLM Routing Techniques: A Deep Dive
On the latest episode of RAG Masters, CEO of Unify AI Dan Lenton explored the technology behind LLM Routing along with hosts Neil Katz and Daniel Warfield.
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Unlocking the Power of Agentic RAG: The Next Step for AI Problem-Solving
In the latest episode of the RAG Masters show, we explore Agentic RAG, different techniques to build, integrate, and evaluate it, real-world use cases, and future challenges the field might face.
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The AI Engineer's Guide to Document Parsing in RAG Applications
Let's start with a fundamental truth: parsing is the bedrock of any RAG application.