TileDB is foundational software designed by scientists for scientific discovery.
Joined September 2017
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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 ๐
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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.
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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.
๐ From ๐๐ถ๐ญ๐ต๐ช๐ฎ๐ฐ๐ฅ๐ข๐ญ ๐๐ข๐ต๐ข ๐๐ญ๐ข๐ต๐ง๐ฐ๐ณ๐ฎ๐ด: ๐ 2026 ๐๐ถ๐บ๐ฆ๐ณ'๐ด ๐๐ถ๐ช๐ฅ๐ฆ
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๐ 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!
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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.
๐ From ๐๐ถ๐ญ๐ต๐ช๐ฎ๐ฐ๐ฅ๐ข๐ญ ๐๐ข๐ต๐ข ๐๐ญ๐ข๐ต๐ง๐ฐ๐ณ๐ฎ๐ด: ๐ 2026 ๐๐ถ๐บ๐ฆ๐ณ'๐ด ๐๐ถ๐ช๐ฅ๐ฆ
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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 ๐
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Petabytes of imaging, genomic, and clinical data โ 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 ๐
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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.
๐ Quote from ๐๐ถ๐ญ๐ต๐ช๐ฎ๐ฐ๐ฅ๐ข๐ญ ๐๐ข๐ต๐ข ๐๐ญ๐ข๐ต๐ง๐ฐ๐ณ๐ฎ๐ด: ๐ 2026 ๐๐ถ๐บ๐ฆ๐ณ'๐ด ๐๐ถ๐ช๐ฅ๐ฆ
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Are you ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ฌ๐ฏ๐ฐ๐ธ? We think you should be.
@TileDB, @TTECDigital and @Microsoft are bringing together biopharma leaders at #BioITExpo โ drinks, real conversation, multimodal data AI-ready.
Your agent's not invited. Promise.
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May 20 | Boston
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Multimodal AI has huge potential in healthcare โ 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 ๐
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Multimodal AI, generative AI, and agentic AI increasingly overlap โ 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 ๐ hubs.la/Q04d-KNt0
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Your AI agent isn't invited. Promise.
At #BioITExpo? We're hosting a private cocktail reception with @TTECDigital and @Microsoft โ a chance to connect with biopharma leaders tackling the challenge of making scientific data AI-ready.
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May 20 | Boston
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In healthcare, no single data source reveals the full picture.
Multimodal AI surfaces the connections โ across genomics, clinical data, and patient records.
Read the complete guide on multimodal AI ๐hubs.la/Q04dHjZL0
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TileDB retweeted
Apr 24
Discover how TTEC Digital and TileDB are delivering cloud-native genomics data platforms to help life sciences organizations from Roberto Llerasโ (VP Healthcare & Life Sciences Solutions and Strategy, @TTECLife) at #FOGBoston. More info: hubs.la/Q04d0JJp0
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Most multimodal AI projects don't fail on the model โ they fail on the data.
Incompatible file formats, inconsistent metadata across labs and vendors. The data exists โ but can't be harmonized.
The complete 2026 guide to Multimodal AI ๐hubs.la/Q04dmnFR0
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Not all multimodal datasets are equally accessible.
Some are open. Others need institutional approval or credentialed licenses โ a detail that determines what's usable.
We mapped 15 โ size, access model, and use case for each.
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Inconsistent metadata and incompatible file formats across imaging systems and electronic health records can hinder AI training in healthcare. Without expert-led curation pipelines, multimodal models risk learning from incomplete or mislabeled data.
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