Tile.ai

The Enterprise AI Data Substrate — the governed layer that activates your data for AI, wherever it lives. By the team behind open-source TileDB.

[Tile.ai @tileai_inc]

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.

https://t.co/PJ7Ow9lnHx

[Tile.ai @tileai_inc]

TileDB is now https://t.co/2ltvWXMWBf.

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 👇
https://t.co/bC40zEbvFG

[Tile.ai @tileai_inc]

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 👇
https://t.co/a8ZBYFS3qt

[Tile.ai @tileai_inc]

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👇
https://t.co/l8FGc1CQiO

[Tile.ai @tileai_inc]

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 👇
https://t.co/32aGSfzvGh

[Tile.ai @tileai_inc]

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 👇
https://t.co/AauqWpVlVG

[Tile.ai @tileai_inc]

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 👇
https://t.co/Vgdciugoa8

[Tile.ai @tileai_inc]

https://t.co/IcEEqYDw0G

[Tile.ai @tileai_inc]

Combining different data types presents substantial technical difficulties for HCLS organizations.

Integrating multimodal data requires custom ETL pipelines, specialized storage architectures, metadata frameworks & ontology mapping.

Read more about multimodal data challenges 👇

[Tile.ai @tileai_inc]

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.

https://t.co/Qc94EAXaET

[Tile.ai @tileai_inc]

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
https://t.co/n89BxesPiE

[Tile.ai @tileai_inc]

🎉 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!

[Tile.ai @tileai_inc]

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
https://t.co/GHPMKY6xOs

[Tile.ai @tileai_inc]

@TTECDigital @Microsoft https://t.co/fgvGA4jXxb

[Tile.ai @tileai_inc]

Gearing up for #BioITExpo in Boston next week. Wednesday, we're hosting a reception in the Seaport with @TTECDigital + @Microsoft.

George Llado, veteran pharma technologist & TileDB board member, will be joining us to discuss multimodal scientific data AI-readiness.

RSVP 👇

[Tile.ai @tileai_inc]

Petabytes of imaging, genomic, and clinical data — and every new modality forces a rebuild.

The question isn't whether you have enough data. It's whether your architecture can query it where it lives.

Read the complete guide 👇
https://t.co/KXGeDvBGJZ

[Tile.ai @tileai_inc]

Multiomics advances. Getting insights to the bedside in real time is a different problem. That gap isn't purely scientific. It's a data infrastructure one.

📖 Quote from 𝘔𝘶𝘭𝘵𝘪𝘮𝘰𝘥𝘢𝘭 𝘋𝘢𝘵𝘢 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴: 𝘈 2026 𝘉𝘶𝘺𝘦𝘳'𝘴 𝘎𝘶𝘪𝘥𝘦
https://t.co/hrtBxaUIUu

[Tile.ai @tileai_inc]

Are you 𝘪𝘯 𝘵𝘩𝘦 𝘬𝘯𝘰𝘸? We think you should be.

@TileDB, @TTECDigital and @Microsoft are bringing together biopharma leaders at #BioITExpo — drinks, real conversation, multimodal data AI-ready.

Your agent's not invited. Promise.

📅 May 20 | Boston
https://t.co/SLEGpkiRyn

[Tile.ai @tileai_inc]

Multimodal AI has huge potential in healthcare — but scaling it faces real barriers: incompatible data formats, multiplying compute costs, modality bias, and explainability gaps that block regulatory approval.

Here's the full breakdown 👇
https://t.co/D8TfIIjZ4a

[Tile.ai @tileai_inc]

Multimodal AI, generative AI, and agentic AI increasingly overlap — but their core purposes differ.

One connects diverse data types. Another produces new outputs. A third acts autonomously. The distinctions shape your AI strategy.

Read more 👉 https://t.co/ill5f1ftGh