Datavid - a C5i company

Many RAG PoCs look promising at first. But in regulated environments, AI does not scale on retrieval alone. It needs quality, structure, governance, and interoperability behind it. In this short video, we show how Datavid helped unify cross-biobank research with an ontology-driven metadata foundation and governed semantic + RAG approach. 👉 Explore GraphRAG services: datav.id/48RsOUF #GraphRAG #EnterpriseAI #RAG #KnowledgeGraph #DataGovernance #LifeSciences

🤖 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

Datavid is heading to KGC 2026! We’ll be attending @KGConference, taking place May 4-8 at Cornell Tech, NYC, and online. Looking forward to conversations on knowledge graphs, semantic data, enterprise AI, and AI-ready data foundations. datav.id/4vTlMZk #KGC2026 #KnowledgeGraphConference #KnowledgeGraphs #SemanticData #EnterpriseAI #AIReadyData #TrustedAI #Datavid

We keep seeing the same pattern in enterprise AI: 💪 Strong pilots. 🏃‍♀️ Real momentum. 🛑 Then… hesitation. Not because the tech fails, but because trust is missing Can you explain it? Trace it? Govern it? 📉 That’s where AI slows down 👉 Read more: datav.id/4cb5UK5 #EnterpriseAI #DataGovernance #GraphRAG #AI #CDO #DataStrategy

Your AI worked perfectly in the demo. Then users started asking real questions. • Same query → different answers • Follow-ups → lost context • Complex questions → shallow responses This is where most AI products start to struggle. In this 90-second demo, we show: – why traditional RAG behaves this way – what changes when context is structured and connected – and how that impacts product reliability 👉 datav.id/4d4QK9q #AI #GraphRAG #ProductManagement #EnterpriseAI #LLM

Datavid joins C5i to strengthen the data foundations behind enterprise AI 🤖 Because the real bottleneck isn’t a model. It’s data. Most enterprise AI still struggles because data is: – Disconnected – Lacking context – Hard to trust That’s what determines whether AI works or not. Because better AI doesn’t start with models. It starts with data. Know more here datav.id/4u3KnsZ #DatavidAC5iCompany #Datavid #C5i #AI #EnterpriseAI #KnowledgeGraphs #AgenticAI #DataStrategy #DigitalTransformation

Trade reporting is becoming a data architecture challenge 📊 See how a leading European bank built a scalable trade data hub to support real-time regulatory reporting and MiFID II compliance ⚙️ Read the case study: datav.id/4dXQkCQ #RegTech #DataEngineering #FinancialServices #MiFIDII

Datavid is now part of @C5i. Strengthening enterprise AI with knowledge graphs, graph data engineering, and semantic data foundations. Enabling more reliable, context-aware generative & agentic AI. #DatavidAC5iCompany #Datavid #C5i #AI #EnterpriseAI #AgenticAI

Scaling doesn’t break systems. It adds overhead. ⚙️ More coordination 🔗 More dependencies 📈 More effort to keep things consistent That’s where things start to slow. More in this month’s newsletter 👇 📘 Subscribe here datav.id/48o5SMs #EnterpriseAI #GraphRAG #DataStrategy

Thank you to the @PistoiaAlliance team for a fantastic London Conference. 🤝 Great conversations with leaders across pharma, biotech, and tech on FAIR data, AI in pharma, and trusted research data foundations. Proud to be part of this community moving life sciences innovation forward! #LifeSciences #FAIRdata #AIinPharma #PistoiaAlliance

Kudos to Sherlin 👏 Her attention to detail and proactive approach during complex testing helped deliver a bug-free production release. 🚀 #TeamDatavid #QualityEngineering #Testing #QA

📍➡️🌐From location-bound studies to scalable remote neuromarketing research See how Neuro-Insight modernized study execution with Datavid. datav.id/4tmwKon #Neuromarketing #ResearchTech #DataPlatforms

🇬🇧 Next stop: London on April 13–14! The Datavid team will be attending the @PistoiaAlliance London Conference to engage with the life sciences community on FAIR data, AI in pharma, and the future of research data platforms. If you're attending, let’s connect 👋 datav.id/4sNpAcy #LifeSciences #FAIRdata #AIinPharma

RAG solved a real problem for enterprise AI. But for anyone dealing with enterprise data at scale, it didn’t fully solve the right one. For the first time, LLMs could work with internal data, and the outputs felt more grounded. But once you move beyond demos, something starts to feel slightly off. Not in an obvious way. The answers aren’t wrong. They just don’t fully land. It comes down to how RAG works. It retrieves relevant chunks of information, but enterprise context doesn’t really live in chunks. It lives in relationships between customers, transactions, products, and decisions. That context is spread across systems, governed differently, and rarely sits in one place. When you break it into pieces, some of those connections get lost. The model fills the gaps, but it’s still stitching things together. That’s why you see answers that look fine on the surface but miss something important. Or similar questions giving slightly different responses. Nothing dramatic, but enough to make you question how reliable it is at scale. The more you look at it, the more it feels like the issue isn’t retrieval. It’s context. That’s where GraphRAG becomes interesting not as a replacement, but as a different way of thinking about the problem. In simple terms, it lets the model work not just with documents, but with how entities and information are actually connected. And that changes things more than you’d expect. Answers carry context across entities. They’re easier to trace, easier to explain, and more consistent across similar queries. Not perfect, but noticeably more reliable, especially in environments where traceability and governance matter. It feels less like an upgrade and more like a shift from retrieving data to actually making sense of it. We’ve spent the last phase of enterprise AI figuring out how to access data. Now it feels like the real challenge is connecting it in a way that’s usable, governed, and scalable. Been thinking about this space a bit more deeply, especially how GraphRAG plays out in real enterprise setups. Sharing a more detailed breakdown here if you want to go deeper: datav.id/4dpcVbe #GraphRAG #EnterpriseAI #DataStrategy #AIArchitecture #DataGovernance

Datavid will be attending the 2026 London Conference hosted by the @PistoiaAlliance . 📍London 🗓️ April 13-14 Looking forward to engaging with the life sciences community on topics like FAIR data, AI-ready research platforms, and semantic data foundations for pharma innovation. If you’re attending as well, let’s connect 👋 datav.id/47iundJ #LifeSciences #FAIRdata #AIinPharma #PistoiaAlliance