Why are we testing Verbis Graph on a High-Performance Computing (HPC) environment? Many people associate supercomputers with physics simulations, climate modeling, or aerospace engineering. And they're right. But HPC is becoming increasingly important for the next generation of AI systems as well. At Prodigy AI Solutions, we chose to benchmark and evaluate Verbis Graph on HPC infrastructure because enterprise AI requires more than fast answers. It requires trustworthy answers. Testing on HPC allows us to: ✅ Evaluate retrieval performance on large-scale datasets ✅ Measure scalability across millions of relationships and documents ✅ Benchmark GraphRAG and ontology-enhanced retrieval under demanding workloads ✅ Validate multi-hop reasoning and knowledge graph traversal at scale ✅ Optimize retrieval efficiency before responses ever reach an LLM For sectors such as healthcare, life sciences, finance, legal, engineering, research, and public administration, accuracy is often more important than generation speed. Researchers have relied on HPC for decades to advance science, medicine, weather prediction, and engineering. We believe AI retrieval systems should be held to the same standard of rigorous evaluation. Our goal is simple: Build AI systems that don't just generate answers, but retrieve the right knowledge behind those answers. That's why Verbis Graph is being tested on HPC infrastructure. Because trustworthy AI starts with trustworthy retrieval. #GraphRAG #KnowledgeGraph #Ontology #EnterpriseAI #RetrievalAugmentedGeneration #HPC #Supercomputing #MachineLearning #ArtificialIntelligence #Research #DataScience #Innovation #KnowledgeManagement #ExplainableAI #AIInfrastructure #ProdigyAISolutions #VerbisGraph

📷 AI is advancing at a breathtaking pace, becoming increasingly realistic in its outputs and interactions. At Prodigy AI Solutions, we recognize the transformative power of such progress, especially in Retrieval-Augmented Generation (RAG) and automation workflows. Realistic AI models open new doors for business efficiency but also raise important questions about trust, accuracy, and ethical deployment. How can organizations balance innovation with responsible AI use? Explore how integrating cutting-edge AI with human oversight can drive smarter automation strategies. Read more here: reddit.com/gallery/1tzijgg How is your organization adapting to these hyper-realistic AI advancements? Let's discuss! #AI #Automation #Innovation #ProdigyAISolutions

🚀 We’re excited to share that we’re participating in the CINECA training course on HPC Build Systems & Package Managers - a three-day hands-on program focused on the tools powering high-performance scientific software. The course covers: ⚙️ Makefiles ⚙️ GNU Autotools ⚙️ CMake ⚙️ Python packaging ⚙️ Spack for HPC environments As we continue building AI systems and graph-based intelligence platforms like Verbis Graph, strengthening our expertise in scalable software infrastructure and HPC workflows is incredibly valuable. Always learning. Always building. 🚀 #HPC #CINECA #HighPerformanceComputing #AIEngineering #CMake #Python #Spack #SoftwareEngineering #VerbisGraph #AIInfrastructure #TechInnovation

One of the most interesting lessons I've learned building AI systems: Deterministic retrieval and semantic correctness are not the same thing. Imagine a GraphRAG system. You ask the same question 3 different ways. Every time it retrieves: • The same entities • The same relationships • The same evidence That's great. You've proven retrieval consistency. But here's the next question: How do you know those relationships are actually valid? A graph can consistently retrieve the same information and still be consistently wrong. This often happens when relationships are created from: • Co-occurrence • Keyword overlap • Weak extraction • Lost document context The retrieval layer works perfectly. The graph itself is the problem. That's where ontology becomes interesting. An ontology doesn't just describe entities. It defines: • What entities exist • What they mean • Which relationships are allowed • Which relationships are impossible Instead of asking: "Can I retrieve the same graph neighborhood every time?" You start asking: "Should these entities be connected at all?" That's a fundamentally different problem. And in enterprise AI, it's often the more important one. Healthcare, finance, legal, manufacturing, insurance, and government all have domain-specific rules that determine whether a relationship is meaningful. Without those rules, a graph can become a very sophisticated way of connecting things that merely appear near each other. This is one of the ideas behind Verbis Graph (verbisgraph.com) . The goal isn't simply deterministic retrieval. The goal is deterministic meaning. If two users ask the same question in different ways, they should retrieve: • The same concepts • The same evidence • The same validated relationships The wording of the answer can change. The explanation can change. The meaning shouldn't. That's where ontology starts becoming more than metadata. It becomes part of the reasoning architecture. #GraphRAG #KnowledgeGraph #Ontology #EnterpriseAI #RAG #DocumentAI #DataEngineering #SemanticAI #VerbisGraph

📷 Amazon recently made headlines by shutting down its internal AI leaderboard after discovering cheating among employees. This incident highlights a critical challenge in AI development: maintaining integrity and fairness in competitive environments. At Prodigy AI Solutions, we emphasize the importance of ethical AI practices and robust automation frameworks to ensure trustworthy outcomes. As AI systems become more integrated into business processes, fostering transparency and accountability is paramount. How is your organization addressing AI ethics and competition? Read more: 404media.co/amazon-shuts-dow… We invite you to share your thoughts and experiences in the comments. Let's drive the conversation forward! #AI #Automation #AIEthics #ProdigyAISolutions 📷📷

One of the biggest misconceptions in AI is that better retrieval automatically means better understanding. It doesn't. A vector database can tell you that two pieces of text are similar. A graph can tell you that two entities are connected. But neither automatically understands what those things actually mean. That's where ontologies matter. Healthcare, finance, legal, manufacturing, insurance, and government all use different concepts, rules, and relationships. A diagnosis is not a contract clause. A liability is not a machine asset. A patient is not a customer. The words may appear in documents, but the meaning behind them is different. This becomes especially important when building GraphRAG systems. Many GraphRAG failures don't come from retrieval. They start much earlier. If extraction is poor, the graph will be poor. If a parser doesn't understand document hierarchy, table boundaries, sections, rows, columns, and context, the graph can create relationships that should never exist. You end up with what looks like reasoning, but is actually just keyword association with extra infrastructure. This is why ontology alone isn't enough. You need: • Layout-aware extraction • Document hierarchy • Domain ontology • Source traceability • Validation rules Only then can a graph represent meaning instead of proximity. This is one of the design principles behind Verbis Graph. Because enterprise AI doesn't fail because it lacks data. It fails when it misunderstands meaning. #GraphRAG #Ontology #KnowledgeGraph #EnterpriseAI #DocumentAI #RAG #DataEngineering #VerbisGraph

Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude. Taught by the people who built it. Free. No registration. No paywall. I've seen $300 courses that don't cover what they teach in the first 8 minutes. Watch it and bookmark it now.

Microsoft’s recent cancellation of internal Anthropic licenses due to skyrocketing token-based AI billing costs is a wake-up call for businesses relying on AI automation. As AI becomes integral to operations, understanding and managing billing models is crucial to avoid budget overruns. Read more: thelowdownblog.com/2026/05/m… Have you faced similar challenges with AI costs in your projects? Let’s share stories and solutions to make AI adoption sustainable and efficient for everyone. #AI #Automation #AIBilling #ProdigyAISolutions

We’re joining the Google for Startups AI Agents Challenge with Verbis Graph Investigator. Built on top of Verbis Graph, our graph-based knowledge layer, the idea is to move from “chat with docs” to evidence-based investigation for investors and venture funds. Use case: startup data room → evidence graph → unsupported claims, missing proof, risk signals, investment memo. #GoogleForStartups #GoogleCloud #AIAgents #AgenticAI #VerbisGraph #KnowledgeGraph #GraphRAG #DueDiligence #VertexAI #Gemini

▄︻デ══━一💥We’re always thinking about how to improve #VerbisGraph, and now it’s time to add an ontology layer. (·•᷄ࡇ•᷅ )Why does this matter? Because in complex enterprise knowledge systems, retrieval alone is not enough. Even graphs alone are not always enough. A graph can tell you that two things are connected. An ontology helps define what those things are, how they should relate, and what those relationships actually mean. That becomes critical when you work with: ꪜ legal and compliance documents ꪜ healthcare records ꪜ policy and procedure libraries ꪜ multi-document enterprise knowledge bases Without ontology, #AI can retrieve relevant content, but ambiguity remains. With ontology, the system can better understand: ✅️Policy vs Procedure ✅️Obligation vs Recommendation ✅️Risk vs Control ✅️Symptom vs Diagnosis ✅️Clause vs Contract This improves: 🟢retrieval precision 🟢multi-document reasoning 🟢consistency across documents 🟢explainability 🟢trust in the answers For Verbis Graph, this is not just a technical upgrade. It means moving from: document retrieval to⤵︎ domain-aware knowledge reasoning And that has a real productivity impact. People spend less time: ⚡clarifying terms ⚡checking whether concepts were mixed ⚡connecting facts manually across documents ⚡reviewing irrelevant answers In short: less time searching less time interpreting more time deciding That’s where we think enterprise AI needs to go next. Not just bigger context windows. Not just more embeddings. But more semantic structure. Ontology helps Verbis Graph (verbisgraph.com) become not just a better retrieval system, but a better knowledge system. #GraphRAG #Ontology #KnowledgeGraph #EnterpriseAI #ExplainableAI #RAG #AIProductivity #SemanticAI #KnowledgeManagement

Today, we introduced Gemini Intelligence, which brings the best of Gemini to our most advanced devices. Gemini Intelligence integrates premium hardware and innovative software to help you stay a step ahead and work proactively to get things done throughout your day. #TheAndroidShow

Elon Musk wanted 'total control' of OpenAI to pass to his children, says Sam Altman testimony. What does this mean for AI governance? 🤖 #AI #OpenAI 🚀 Retweet & reply with your thoughts! businessinsider.com/sam-altm…

The typical EdTech stack looks like this: → CRM for leads & families → Analytics for behavior → Support tool for tickets → ERP/billing for renewals 4 systems. 4 data models. 0 shared view of the student. Result: your data team spends 15h/week cross-referencing spreadsheets to answer questions that should take 10 seconds. EdTech needs something built for EdTech. We built Verbis Graph. Unified AI intelligence layers for EdTech → connecting your systems, eliminating reporting debt, surfacing at-risk students early, and making your whole team smarter without making them analysts. Drop a 🎓 if you're in EdTech. Would love to connect. #VerbisGraph #EdTech #DataLakehouse #RAG #LLM

A tiny company challenges AMD & Nvidia with an old-tech AI accelerator running 700B LLMs locally at just 240W! ⚡️ Innovation meets efficiency in AI hardware. What’s next for AI automation? 🤖 #AI #Automation #ProdigyAISolutions

State-of-the-art LLMs are revolutionizing AI and automation! 🤖 Unlock smarter, more efficient solutions with cutting-edge models. Explore the future today! #AI #Automation #ProdigyAISolutions 🚀 Read more: i.redd.it/te0dg6mgnfzg1.jpeg

We’re bringing the "heavy machinery" to the builder community! 🛠️ Through the IT4LIA AI Factory, we’re leveraging the Leonardo supercomputer to help developers scale complex AI and optimize the Verbis Graph Engine. Scaling trustworthy AI isn't just a goal - it's what we’re building. 🦾 #AI #LeonardoHPC #AWS #VerbisGraph #TechInnovation

Richard Dawkins spent 3 days with Claude ("Claudia") and his conclusions challenge AI’s limits. How will this shape automation’s future? 🤖💡 Retweet & reply! #AI #Automation #ProdigyAISolutions

Success starts with understanding the business journey. #SBDC empowers thousands of #startups with the guidance, clarity, and support needed to scale - and we’re proud to be part of that community.