ApertureData

New website, fresh documentation, and a MAJOR release on deck! Managing multimodal data is a challenge, but we’re making it easier with our newly launched ApertureDB Cloud. With experience deploying ApertureDB to Fortune 100 customers, we now bring the features to , ApertureDB Cloud which enables faster insights and streamlined workflows. Big thanks to @VentureBeat & @mr_bumss for featuring the release!! Key Figures: •35x faster at mobilizing multimodal datasets •2-4x faster than other some open-source vector search solutions •66% of enterprise data remains unused—time to change that. venturebeat.com/data-infrastru…

Something we understand very well here!

On episode 54 of Generationship, @rachelchalmers is joined by Vishakha Gupta (@vishakha041) of @ApertureData to discuss how AI systems break down in the real world, and how to fix them. They dive into multimodal data, graph databases, and the challenges of unifying complex data pipelines for enterprise use. Tune in! hubs.ly/Q04bQ0Ch0

The real hurdle going forward? Moving from Storage to Memory. Agents don't just need a place to store & retrieve data; they need a unified engine for Vector + Graph + Multimodal context and memory in real-time. That’s the "cognition infrastructure" we are building at @ApertureData (3/4)

On episode 54 of Generationship, @rachelchalmers sits down with Vishakha Gupta (@vishakha041) of @ApertureData to examine the intersection of AI, data infrastructure, and human memory. They explore how multimodal data, graph structures, and vector search must come together to support next-generation AI systems. Tune in! hubs.ly/Q04bL4Y30

Understanding "What" is out there is step one. Step two is the "How." In Part 2c, we will share our observations and guide on how to select, and ask the 8 critical questions every architect must ask before committing to a Cognition stack. Full Audit here: aperturedata.io/resources/ai-m…

I spent the last few weeks auditing 20+ AI memory frameworks. The good news: The "Digital Attic" era is ending. We’re finally seeing real work on connective tissue and reasoning layers. The bad news: We’re trading one bottleneck for three new ones. 🧵

The discussion around surfacing dark data and the improvements in multimodal models is where ApertureDB can fit in and fill the gap with it's support for different modalities, efficient searches, and the ability to build a scalable agentic memory layer on top!

On episode 54 of Generationship, @rachelchalmers sits down with Vishakha Gupta (@vishakha041) of @ApertureData to explore the hidden infrastructure challenges behind modern AI. They unpack why multimodal data systems are still fragmented, how graph and vector approaches can be unified, and what it takes to build production-ready AI pipelines. hubs.ly/Q04bGzs_0

Had a great time chatting with @rachelchalmers about multimodal systems, memory, and the gaps we’re still working to close in AI infrastructure. @ApertureData

Which AI memory framework should I pick? Well… glad you asked 👇 Here is the Part 2a of our landscape study with @alinahm from @tribecap : The Path to Machine Cognition. We’re using the KMC Blueprint to help you make sense of the 2026 market: - Knowledge (the library) - Memory (the growth layer) - Context (the connective tissue) Stop just 'storing data' and start building Cognition. Stay tuned for Part 2b coming next week: The full analysis of 20+ frameworks, from local bots to the Memory Mesh! aperturedata.io/resources/the-…

The next generation of reasoning agents needs a persistent, evolving knowledge, memory, and concept structure, not just a "fetch" command. We’re at #GoogleCloudNext to discuss building this cognitive layer for the enterprise. Let’s find a whiteboard and get into the systems architecture of it. DM @vishakha041 to find a corner to chat.

Knowledge isn't just text. It’s the relationship between a technical schematic, a transcript, and a product specification. Fragmenting vectors, metadata, and data into different silos creates a massive "data tax" in latency and complexity. The future of the stack is unified, multimodal, and graph-aware.

Is context just a decision trace, or is it something more? At @GoogleCloud Next, we’re moving past the "vector search" hype to talk about the actual architecture of AI Cognition. If you're building agents intended for production, the "digital attic" approach to memory won't scale. #GoogleCloudNext

Adding ApertureDB as a substrate to building this!

Found this open-source repo on memory management for AI agents and other workflows... github.com/vishakha041/ap… Things I liked about it: 1. Knowledge 2. Context 3. Memory (of course) def worth a shot ..

Memory is not a feature you bolt onto agents. It’s a data + infrastructure problem: how information is stored, connected, retrieved, and updated over time. We are still early in figuring this out. And in reality, what AI needs is - knowledge, memory, and cognition!

Everyone is talking about AI memory and very few people are building it correctly.

I was learning about 10+ new AI tools a day and realized I had no way to filter the noise. The spark for a better map came from the agentic stack sessions at @PWVentures . Watching founders build real-world "plumbing" forced a shift in perspective. 1/4 🧵

I’ll be at @NVIDIAGTC next week. Jensen Huang’s talked about AI as a “five‑layer cake”: energy → chips/compute → cloud infrastructure → models → applications, and it's becoming the industry’s shared map. What’s interesting is how quickly the software layers are exposing a gap: 𝘁𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝗮 𝗱𝗮𝘁𝗮 + 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘂𝗯𝘀𝘁𝗿𝗮𝘁𝗲 𝘁𝗵𝗮𝘁 𝗴𝗶𝘃𝗲𝘀 𝗔𝗜 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆, 𝗴𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝗼𝘃𝗲𝗿 𝗿𝗲𝗮𝗹‐𝘄𝗼𝗿𝗹𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. Verticalization is accelerating this shift. Physical AI is pushing it even faster. Stateless prompts don’t cut it when systems need to see, remember, and act across time. A few questions I’m bringing into GTC: • Do small, domain‑native models win once memory becomes the anchor, or do frontier models keep stretching upward? • How long can teams absorb token costs before architecture becomes the real constraint? • And what exactly is “memory” now — a log file, a vector store, or the core infrastructure layer that binds the stack? At @ApertureData, we’ve been thinking about this layer for a long time. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗶𝘁 𝘁𝗼𝗼, 𝗹𝗲𝘁’𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁.