Whatever you're building with AI, the knowledge it needs is already in your company's files. Deasy gets it ready in seconds. Most teams have built solid pipelines and agent frameworks — but still have no good way to prepare the data that powers any of it. Is this file relevant? Is it safe? Has it been enriched so AI can find the right answer? Without that, even the best systems underdeliver. Deasy maps your data, removes what shouldn't be there, and enriches everything with the context AI needs. The result is a perfect dataset for whatever (and however) your team is building with AI. Better answers. Sensitive data kept out. Knowledge that stays fresh. Built for enterprise scale.
External link for Deasy Labs by Collibra
New York, US
Love to see the packed room for Reece Griffiths at Big Data LDN. 💥
Raise your hand if SharePoint has become a dumping ground of company knowledge that you now want to use for AI 👀 If you're at Big Data London and wish to bond over the pain of missing metadata, old file versions, and untrustworthy copilots — do come and say hi!
Reece Griffiths talking about governing unstructured data? Our favorite topic.
Your database is governed. Your Slack threads, PDFs and policy docs? Not so much. Most of the context your AI agents pull from has no owner, no freshness date, no lineage. And agents don't flag that. They just answer with total confidence. Kevin Petrie, VP Research at BARC, and Isaac Sacolick, President of StarCIO, unpack what it actually takes to govern the documents and chats your agents depend on. Moderated by Reece Griffiths, Field CTO at Collibra. Context Governance 201: How Do You Govern Unstructured Data for AI Agents? 📅 Sept 23 | 11 AM ET Register → https://ow.ly/AqcQ50ZNZzW #AIGovernance #UnstructuredData #EnterpriseAI #DataStrategy
We built this hands on lab because "clean your data first" is easy to say and hard to do, especially when the data is unstructured. Join us this fall at one of the two Collibra AI Summits.
Your unstructured data is a mess. Bring it anyway. At the Collibra AI Summit, we're not just talking about AI-ready data. We're building it live. In the Deasy Labs by Collibra hands-on lab, bring your own unstructured data and we'll help you run a full quality check, filter out what shouldn't be there and enrich what's left. You'll walk out with a dataset built for better retrieval, fewer wasted tokens and answers you can trust. (Pre-registration is required.) And that's just the warm up. On the main stage, we're revealing what's next for context and control in enterprise AI, with new capabilities you haven't seen yet. Two cities. One day that will change how you think about AI-ready data. 🗓️ New York — October 6 🗓️ London — October 21 Register today: https://ow.ly/XgRJ50ZHNXK #CollibraAISummit #AIGovernance #UnstructuredData #DataGovernance
Retrieval pulls in whatever sits near the answer, and sometimes that's customer data that has nothing to do with the question. We saw it on almost 1 in 4 questions: a real customer's name, email or case number, pulled into the model's context alongside the answer. The model didn't repeat any of it back, but the data was already exposed the moment it entered the prompt. Deasy tags what's sensitive and keeps it out of retrieval, taking exposure to zero. If this is relevant to what you're building, you can book a chat with our team here → https://ow.ly/vosF50ZEM3c #AIReadiness #RAG #DataGovernance #EnterpriseAI
RAG systems can't tell document versions apart. In your enterprise corpus, the correct and current version of a document is in there. But so is an outdated version, and also every draft in between. All of these versions sit in almost the same spot in embedding space, so retrieval pulls old versions alongside the current one. Both are in the context window, and the model has no reliable way to tell which one is the right one. In our benchmark, the old version outranked the current version on 45.5% of queries. So what's it worth to fix it? Detecting the latest version of every document and keeping the outdated copies out of the index took accuracy from 27.2% to 39.7% — a 46% relative gain.
Getting unstructured data ready for AI can take a whole quarter. Or(!) you can spend 45 minutes with Reece Griffiths next week and see the shortcut. Save your seat → https://ow.ly/YYBz50Zn5eY
If you have unstructured data that you want to put to work on a problem that your team is solving with AI, Deasy can help. You'll get: - Better accuracy - Hundreds of hours saved in manual tagging and enrichment work - Better data utilization - Lower compliance risk If you missed Reece Griffiths talk at Collibra's Data Citizens on the Road in San Francisco, we saved you this clip.