EyeLevel.AI

RAG on Prem: Built for Regulated Industries. Enterprise-grade security, accuracy and scale.

[EyeLevel.AI @eyelevelai]

One of our customers got an early look at GroundX Workflows and man the results were even crazier than we thought.

https://t.co/AaMotNPF6p

[EyeLevel.AI @eyelevelai]

@svpino has the bravery to speak truth. LLMs still make mistakes. Agents compound them. Testing is very hard and can't be done by using other LLMs.

That doesn't mean agents are not an amazingly powerful paradigm. They are. Just be clear eyed about what they can and can't do well and keep them tightly bound to ground truth in your documents.

[EyeLevel.AI @eyelevelai]

We’re excited to share that our Co-Founder & CEO, Ben Fletcher, will be speaking at the Inspiration AI Forum this Thursday, August 21 as part of the Applied AI Forums virtual series powered by https://t.co/EiNalBpvm5.

He’ll be diving into real-world Retrieval-Augmented Generation (RAG) applications and how practitioners are shaping the future of accurate, secure, and scalable AI for regulated industries.

Hosted by Inspiration Ventures, this forum is designed for founders, builders, investors, and anyone looking to stay ahead in AI innovation.

Register today:
https://t.co/MNVkvlDg60

@insprVC

#RAG #RetrievalAugmentedGeneration #LLM #EnterpriseAI #AppliedAI #AIInnovation

[EyeLevel.AI @eyelevelai]

LLMs generate answers, but where did the information come from?

Clickable in-text citations let you trace each part of a response back to its source document.

It's a clear step toward transparency and trust in document-grounded AI.

Check out the full video now:
https://t.co/PrlNETmGN3

[EyeLevel.AI @eyelevelai]

LLMs generate answers, but where did the information come from?

Clickable in-text citations let you trace each part of a response back to its source document.

It’s a clear step toward transparency and trust in document-grounded AI.

Check out the full video now: [YouTube link]
#RAG #LLM #Citations #AItransparency #GroundX #GenAI #EnterpriseAI

[EyeLevel.AI @eyelevelai]

We’re proud to partner with Roosevelt Road Specialty to launch FraudX, a new AI-driven approach to insurance fraud detection.

Roosevelt Road is using FraudX to flag potentially fraudulent claims early, streamline investigations, and speed up legitimate payouts. It’s a smarter, faster way to protect policyholders and reduce risk, built with both AI and human expertise.

Read more about how they're putting FraudX into action: https://t.co/GsIQHRgAgu

[EyeLevel.AI @eyelevelai]

The Illusion of Thinking: Apple's Flawed Paper That Pushes the Conversation Forward

Apple’s paper critiques reasoning models and it’s causing a stir. Models trained to "reason" failed basic problems like the Tower of Hanoi.
But instead of dismissing the paper, we think it’s a necessary challenge. It forces us to ask: what does thinking really mean?

In this video, we break down the failures, the backlash, and why this imperfect paper still moves the conversation forward.

Check out the full video: https://t.co/1e74KwQ8bl

[EyeLevel.AI @eyelevelai]

Context windows are getting bigger.
Some say RAG is dead.
But most real-world use cases still blow past a million tokens.
RAG is alive, and still essential.

Links
🔗 Learn more about EyeLevel: https://t.co/K0v3yhajVm
🔗 Follow our open source RAG work: https://t.co/QEzZQvDm7G

#RAG #LLM #GenAI #AIInfrastructure #ContextWindow #VectorSearch

[EyeLevel.AI @eyelevelai]

Today on Rag Masters, we’re diving into MCP vs A2A.

Both are emerging protocols for building agentic applications.
Which one should reign supreme? Or can they coexist?

We break down what each protocol actually does, where they shine, and how they differ at a fundamental level. If you're building AI agents, this one's for you.

Watch the full breakdown: https://t.co/pLAUdrLjeO

#RAG #AIagents #MXGP #A2A #AutonomousAgents #AgenticAI #AIstack #LangChain #AIprotocols #MLOps #RagMasters #AIengineering #LLMs #openai #aiinfrastructure

[EyeLevel.AI @eyelevelai]

RAG vs CAG: Which is better??

CAG is fast and great for small apps, but it’s working with short memory. Don’t expect it to handle millions of documents anytime soon.

RAG taps into external sources, making it better for large, evolving knowledge bases. It’s built for scale.

CAG is promising, but still new. Accuracy is a big question mark. Our take?

The future is both.

Watch the full breakdown: https://t.co/lLnkwlApSb

#RAG #CAG #LLM #GroundX #AI #GenAI #EnterpriseAI

[EyeLevel.AI @eyelevelai]

Want to build RAG on-prem?

In this video, we walk through how to build a fully on-prem RAG system, from ingestion to eval, without relying on external APIs.

Watch the full walkthrough: https://t.co/zNTr6Jh5Qj

#RAG #LLM #OnPremAI #EnterpriseAI #GroundX #AIDevelopment #GenAI #VectorSearch

[EyeLevel.AI @eyelevelai]

What is DocBench and why does it matter for RAG evaluation?

In our latest video, we break down how GroundX performed against OpenAI, Anthropic, and even humans.

These results aren’t just impressive, they show what’s possible when document understanding actually works at scale.

Watch the full breakdown on YouTube: https://t.co/VdaiXzOsBr

#RAG #LLM #DocBench #AIEvaluation #MultimodalAI #GroundX #EnterpriseAI

[EyeLevel.AI @eyelevelai]

What is fine-tuning, really?

This infographic breaks down when to use it and walks through the fine-tuning process from a RAG developer’s perspective.

Think of it as a crash course, reshaping your model to specialize in a new domain.

In RAG Masters, Daniel Warfield calls it:

“Descending down the landscape.”

For more check out our blog: https://t.co/QTZLnqNgBN

#RAG #LLM #FineTuning #GenerativeAI #AIengineering #MLops #ArtificialIntelligence #AI #LLMs #GenAI #AIAgents

[EyeLevel.AI @eyelevelai]

How do you build your own LLM test set for document QA?

From collecting content to generating reliable Q/A pairs and running ablations.

We break it down.

For more check out our blog: https://t.co/DwhnBt6HL6

#RAGapps #LLMtesting #DocumentQA #AIevaluation #EnterpriseAI #GroundTruth #RetrievalAugmentedGeneration #MLOps #AIforDocs #GenerativeAI #LangChain #PromptEngineering #AIExplained

[EyeLevel.AI @eyelevelai]

How are LLMs evaluated for quality and accuracy?

Each method plays a different role.

Pairwise comparisons reduce noise, human evaluation ensures reliability, and LLM-as-a-judge offers scale, but with trade-offs.

Understanding these approaches is important for evaluating RAG and agentic systems effectively.

Explore the full breakdown in our blog:
https://t.co/sV8N7sZCi9

[EyeLevel.AI @eyelevelai]

How does context actually get into an LLM?

From parsing and chunking to prompting and completion, each step is important, and each comes with its own risks.

Understanding the flow means building better, more reliable RAG systems.

Check out our blog for more:
https://t.co/b8Rz7QhHhm

[EyeLevel.AI @eyelevelai]

GroundX on Prem is now live on the Red Hat marketplace for Open Shift. We're beyond excited to partner with the open source legends.

https://t.co/i1qOccJMmX

[EyeLevel.AI @eyelevelai]

Dirty Doc of the Day: An Adobe Photoshop Manual

Can your RAG on Prem handle this?

This isn’t just a user guide. It’s a dense manual full of tools, features, and instructions that support training, troubleshooting, and onboarding.

In design and support workflows, structure and retrieval are everything. In this video, we show how GroundX processes a real Photoshop manual into structured, searchable, LLM-ready data running on prem, air gapped, or in hybrid cloud.

Head over to our YouTube channel for the full video:
https://t.co/mXIEVQR26q

[EyeLevel.AI @eyelevelai]

Recently our GroundX RAG platform topped the DocBench leaderboard for RAG accuracy on complex docs.

But having @svpino highlight it is as much of an honor.

[Santiago @svpino]

We are now at the superhuman AI phase!

@eyelevelai's GroundX, and enterprise-grade RAG system, outperformed humans on DocBench — a benchmark that tests deep document comprehension.

GroundX is an open-source system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network.

(If the military wants to do RAG, this is precisely what they will be looking for.)

They offer two services you can use:

1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base.

2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base.

The combination of these services can read and understand documents better than humans! (Here, we're talking about legal documents, medical records, and financial reports.)

While your average AI still fumbles tables and misreads figures, GroundX crushes multimodal and textual questions with 90–95% accuracy.

That's superhuman level on one of the hardest document tasks out there!

And this is not just about extracting text, but about understanding the structure of a document, its visuals, context, and nuance.

The funny thing is that GroundX has almost saturated the benchmark. We'll need better benchmarks if we want to keep measuring progress.

This is huge for RAG systems.

Remember that RAG isn't just about finding the right chunk of text. RAG is about delivering high-quality, context-aware answers from complex documents.

GroundX's progress makes it really hard to beat it at this game.

[EyeLevel.AI @eyelevelai]

4 Common Failure Points in Document-Contextualized AI

Getting document-grounded AI right is harder than it looks.

From broken parsing to fragile search, small cracks lead to big failures.
This visual highlights where things go wrong and why strong foundations matter.

Learn more at https://t.co/QPW3YXlXgy.