Datavid - a C5i company @DatavidML

Simple days, great memories. 😊  This week’s London get-together with the Datavid and C5i colleagues was all about the good stuff: office catch-ups, cakes, lunch, and the classic after-work drinks and dinner!  A great reminder that some of the best team moments come from simply spending time together!  #TeamDatavid #C5i #TeamCulture

Compliance is not the enemy of AI speed. Ungoverned data is. 💡 Build AI on governed, auditable data and policy becomes your moat, not your blocker. How is your organization turning compliance into an advantage? 👇 #AIGovernance #DataCompliance #EnterpriseAI #Datavid

3 questions heading toward every firm deploying AI in regulated environments: 1. Can your data keep up? 2. Who controls what AI agents have access to? 3. Can you replay a decision on demand? Most firms can answer one. ⚠️ @BalvinderDang breaks it down 👇 datav.id/4nmvfV9 #AIGovernance #RegTech #FinancialServices

Research integrity checks are most effective before peer review begins, not after publication. Trust Signals helps editors screen authors, manuscripts, references, and affiliations using 30+ trust markers and an explainable Trust Score. Explore: datav.id/4tD3nxP #ResearchIntegrity #Publishing

Every regulatory failure traces back to the same place. Not bad tech. Not bad intentions. The data layer. 🚨 Our CEO @balvinderdang on why 👇 datav.id/42sg5Ek #RegTech #DataGovernance #FinancialServices

Semantic Data New York 2026 takes place October 1 in New York, co-located with DAM New York Join practitioners exploring #taxonomy, #ontology, #knowledgegraphs and AI-ready data systems with practical insights & networking SAVE $300 ends June 26 #SemanticDataNY #SemanticData2026

If your AI feature feels “almost there” but never reliable… 🤔 Demo = ✨impressive Production = ⚠️unpredictable Users don’t complain — they just stop trusting it The issue? RAG treats every query like a fresh start. But users: • ask follow-ups • refine questions • expect continuity That gap = broken experience. GraphRAG fixes this with connected context. Less firefighting. More building 👉 Full breakdown: datav.id/4vZOojL #AI #GraphRAG #ProductManagement

Siloed research. Inconsistent clinical data. AI strategies with no foundation to stand on. 🔬 In life sciences, fragmented data does not just slow teams down. It delays discovery, breaks compliance, and holds innovation back. That is where Datavid can help. 💡 Structured. Searchable. AI-ready. Built for life sciences teams ready to move faster. Find out what is possible for your team. datav.id/4mV9v2y #LifeSciences #DataStrategy #AIReady #SemanticSearch #RegulatoryCompliance #Datavid

RAG works for clean, document-based data 💬 Enterprise data isn’t that. It’s messy, connected, and dependent 🔗 That’s where RAG breaks ⚠️ Not a model problem. A structure problem. Fix → ground it in a knowledge graph 🧠 datav.id/4tFnw7f #GraphRAG #EnterpriseAI

Legacy infrastructure does not announce itself as a problem. It just quietly costs you. ASTM fixed it. Here is how: datav.id/3RjpE6g #CDO #DataEngineering #DigitalPublishing #Datavid

Data quality isn’t a tech problem. It’s a business outcome problem. You feel it when: → audits get uncomfortable ⚠️ → reports don’t match → AI isn’t trusted 🤖 That’s not pipelines failing. That’s credibility breaking. We see this pattern often with regulated teams. More here 👇 datav.id/4tGAhOU #DataQuality #AI #Compliance #DataGovernance

How was this number produced?" Most financial institutions cannot answer that question from their systems. They rebuild it. Every time. 🔄 We explored why this keeps happening and what the fix looks like 👇 datav.id/4ff8uAt #RegTech #FinancialServices #DataArchitectur

The real AI productivity problem is not generation. It is verification. One of the strongest themes we took away from @KGConference was that enterprise AI productivity is not just about creating more output. It is about making sure that output can be trusted. AI can now produce drafts, summaries, reports, recommendations, and answers at speed. But in business-critical environments, speed is only useful if the output is accurate, complete, compliant, and traceable. Someone still needs to check: Is this correct? Where did the answer come from? Is the source reliable? Can we explain it to a regulator, customer, scientist, editor, or internal decision-maker? In regulated and knowledge-intensive industries, that verification burden can quickly reduce, or even cancel out, the productivity gain. This is where knowledge foundations matter. Knowledge graphs, semantic layers, metadata, provenance, and governed retrieval help move AI from “more output” to better, more trusted outcomes. Because the goal is not simply to generate faster. The goal is to make enterprise knowledge easier to find, understand, reuse, and trust. Explore how Datavid helps organizations build AI-ready data foundations: datav.id/4ueJf60 #KnowledgeGraphs #EnterpriseAI #SemanticData #GraphRAG #AIReadyData #DataGovernance #TrustedAI #KnowledgeGraphConference #Datavid

🤖 Your AI is generating answers. ⏳ Your team spends the next hour figuring out if it can be trusted. That is not a model problem. It is a knowledge problem. The bottleneck in enterprise AI is not generation. It is verification. 📩 More in this month’s newsletter, subscribe now: datav.id/438FFOT #EnterpriseAI #GraphRAG #DataStrategy

First-pass manuscript checks are becoming a bottleneck. Managing editors are dealing with more submissions, messy metadata, author checks, reference verification, and integrity concerns, often across disconnected tools. The problem is not just speed. It is consistency. Trust Signals helps editorial teams surface submissions that need closer attention, with clearer and more explainable integrity signals. See how it supports first-pass checks: datav.id/4u9p21G #ResearchIntegrity #ScholarlyPublishing #EditorialWorkflow #TrustSignals

Thank you, @KGConference 2026! It was great to attend and connect with leaders exploring how knowledge graphs, semantic technologies, and governed data foundations can make enterprise AI more trusted and scalable. Thanks to the organisers, speakers, and everyone we met. #KGC2026 #KnowledgeGraphs #EnterpriseAI

Enterprise AI doesn’t fail because of the model. It slows down when organisations try to scale it. Can it be trusted? Explained? Governed? That’s where many initiatives stall. 👉 Read the blog: datav.id/4uvKpd4 🎧 Podcast version also available: On the blog + YouTube → datav.id/48KXiaT #EnterpriseAI #GraphRAG #AI #DataGovernance

18 months in, still not live. Sometimes, the bravest call a CDO makes is admitting the current approach is not working. Standards Australia made that call. Read what happened next: datav.id/42mNmkq #CDO #CloudMigration #DigitalPublishing #Datavid #StandardsOrganizations

Most enterprise AI failures aren’t model problems 🔍 They’re relationship problems. No structure → confident hallucinations ⚠️ In regulated industries, that’s a risk you can’t take. Ground it in a knowledge graph 🧠 More: datav.id/4w3Yosu #GraphRAG #EnterpriseAI