Your AI agents can't use data they can't find. When data is spread across teams and their storage buckets, warehouses, and SaaS tools, most of what you own never makes it into the answer, prior research included. Here's how the Enterprise AI Data Substrate makes all of your data discoverable: hubs.la/Q04BfVl40

Your data sits in silos across formats, teams, and services. Agents see only a fraction of what you own. The Enterprise AI Data Substrate activates all of it in place, for your AI. Here's what it is 👇 hubs.la/Q04B0mQN0

Enterprise AI keeps stalling between pilot and production. The gap isn't in the models. It's in the data underneath them. Closing it takes a new layer between your data and your AI. Introducing the Enterprise AI Data Substrate 👇 hubs.la/Q04zfxQ10

The gap between what enterprises expect from AI and what they get is a data problem. Models are commoditizing. Success now rests on how usable and reachable the underlying data is. Our CEO explains: hubs.la/Q04yWFtV0

As a Chief Information Officer, George Llado kept coming back to one problem: making enterprise data usable. AI made it impossible to ignore. So he joined Tile.​ai as CEO to take it on. hubs.la/Q04yy5dz0

Nine years ago, Stavros Papadopoulos, our founder and Chief Product Officer, set out to solve a problem most people worked around: data that didn't fit the tools built to manage it. Then AI asked more of that data. hubs.la/Q04y2nGp0

Announcing a strategic investment from @Amgen Ventures and existing investor Two Bear Capital to execute on our vision for the Enterprise AI Data Substrate: the governed layer that activates your data for AI, wherever it lives. hubs.la/Q04xKw_-0

TileDB is now Tile.ai. Today, we are introducing the Enterprise AI Data Substrate: the governed layer that makes all your data AI-ready, wherever it lives. Read the announcement 👇 tile.ai/blog/tiledb-is-now-t…

Proof-of-concept and production are different problems. Moving multimodal systems into production introduces new demands on reliability, monitoring, and maintenance — and the complexity of operationalizing them is often underestimated. Read more 👇 hubs.la/Q04rNjJ-0

Multimodal data requires expertise across multiple domains. Yet organizations often struggle to find talent with the breadth needed to work across text analytics, image processing, time-series analysis, and other domain-specific methods. Read more👇 hubs.la/Q04prpQx0

Multimodal data initiatives typically require significant upfront investment before delivering business value. Executives often struggle to develop compelling business cases with traditional ROI frameworks. Read more about multimodal data challenges 👇 hubs.la/Q04nJ-b_0

Data quality varies significantly across modalities — different noise profiles, missing data patterns, reliability characteristics. Temporal and spatial alignment between modalities adds more. Read more about multimodal data challenges 👇 hubs.la/Q04mj_6C0

High-resolution imagery, video, and scientific data demand enormous storage and computational resources. Distributed storage. Tiered strategies. Parallel computing. Edge processing. Read more about all multimodal data challenges 👇 hubs.ly/Q04lQVtZ0

A patient isn't a row in a database; it's demographics, clinical notes, imaging, time-series vitals and genomic sequences combined. Each modality demands different storage & query patterns. Multimodal data is a design problem before it's an analysis one. hubs.la/Q04jdwBX0

Years of pharma research live in scattered documents that no AI agent can use. That's the silo problem multimodal data platforms must solve, not work around. 📖 From 𝘔𝘶𝘭𝘵𝘪𝘮𝘰𝘥𝘢𝘭 𝘋𝘢𝘵𝘢 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴: 𝘈 2026 𝘉𝘶𝘺𝘦𝘳'𝘴 𝘎𝘶𝘪𝘥𝘦 hubs.la/Q04hSXX30

🎉 Couldn't imagine a better afternoon to top off #BioITWorld. We brought R&D leaders to Boston's Seaport to make multimodal scientific data a substrate AI agents can find, access, and trust. A heartfelt thanks to our co-hosts @TTECDigital and @Microsoft. Until the next one!

In a competitive biopharma market, multimodal omics data powers innovation. But the infrastructure and tools to work with it are usually built as separate stacks. 📖 From 𝘔𝘶𝘭𝘵𝘪𝘮𝘰𝘥𝘢𝘭 𝘋𝘢𝘵𝘢 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴: 𝘈 2026 𝘉𝘶𝘺𝘦𝘳'𝘴 𝘎𝘶𝘪𝘥𝘦 hubs.la/Q04hvpb10