An architecture walkthrough, an early look at an internal tool actually being built, and some of the most experienced builders in agentic memory in one room. San Francisco, October 13.

Memory is the dessert everyone in AI wants right now. Episode 1 of The Cognitive Layer makes the case for eating your vegetables first, the data foundation underneath it. Himanshu Singh (Netflix) on why entity resolution is a systems problem, not a prompt problem, and what happens when you skip straight to "memory": agents don't just make mistakes, they hallucinate in sync.

The data platform pain is real, and so is the choice sitting right in front of you. Stitch together five systems and hope, or build on something that already works. Our customers keep picking the second path, and it's why they can focus on outcomes instead of infrastructure.

Understanding multimodal data isn't just about storing it. It's about being able to pull out the one paragraph, bounding box, or clip that matters, without paying for the whole blob every time. We explored array-based formats early on. The problem wasn't storage itself, it was that the overhead negated the exact benefit you'd expect from fast partial access. You'd gain on retrieval logic and lose it right back to bloat (loss of caching). The deeper issue: images, video, and documents don't need to come back in entirety most of the time. A user wants a bounding box, not the whole image. A clip, not the whole video. A paragraph, not the whole document. That requires a format built around a one-to-many relationship, one asset, many meaningful parts, with fast access to just the part that matters. That's what pushed us toward the graph. Two things fell out of that decision: • Clips, bounding boxes, and paragraph spans live as metadata and relationships, not duplicated bytes. No re-encoded copies of the same video for every clip someone wants. • We can embed the relevant part directly, not just the whole asset, because the whole often loses the detail a specific query actually needs. Native multimodal support also means real processing, chunking, resizing, format conversion, happens at the point of retrieval, not as a separate pipeline someone has to build and maintain. That's the actual bet behind multimodal native: not storing more data types, but understanding what's inside them well enough to hand back only the part someone needs!

A team came to us mid-migration, moving off a stitched-together stack because every quarter their query latency got a little worse and nobody could tell them why. Turned out the problem wasn't any single piece. It was three: a vector store, a graph store, and the glue code holding them together, each optimized on its own, none of them designed to talk to each other under real production load. Nobody could tell them why because debugging meant tracing a query across three systems that each told a slightly different story. We rebuilt their retrieval path on ApertureDB: one engine, embeddings, graph, and multimodal data together, no format conversion between systems, no glue code translating one store's output into another's input. In our benchmarks: 65K+ nodes/second ingestion, 1M+ QPS retrieval. That's the part that actually matters. Fast writes vs. fast reads vs. scale isn't a law of nature, it's usually a tax you pay for plumbing: converting formats, reconciling three systems that were never designed to agree. Remove the forced plumbing, and the tradeoff you thought you had to make mostly wasn't real to begin with. Debugging and maintenance stopped being their growing pains. That's the part worth paying attention to.

Built from the start, not bolted on after. Here's why that distinction matters for agent memory at scale.

Our founder Vishakha on why memory that only stores what isn't enough. Relationships and provenance are the parts most systems skip.

𝗔𝘀 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗺𝗼𝗿𝗲 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹, 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝘁𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗹𝗮𝘆𝗲𝗿 𝘁𝗵𝗮𝘁 𝗽𝗼𝘄𝗲𝗿𝘀 𝘁𝗵𝗲𝗺. In a recent episode of AI Chronicles, Manasvi Sharma from Gracenote (a @nielsen company) highlighted the growing importance of 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗴𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗻𝗱 𝗴𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴. 𝗛𝗲 𝗮𝗹𝘀𝗼 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗱 𝗔𝗽𝗲𝗿𝘁𝘂𝗿𝗲𝗗𝗕 𝗮𝘀 𝗮𝗻 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗲𝗱 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗽𝗮𝗰𝗲. His point resonates with what we're seeing across the industry: understanding relationships between data can be just as important as retrieving similar data. As teams build more sophisticated AI workflows involving images, video, audio, text, embeddings, and metadata, the ability to connect modalities while maintaining context becomes increasingly critical. 𝘛𝘩𝘢𝘯𝘬𝘴 𝘵𝘰 𝘔𝘢𝘯𝘢𝘴𝘷𝘪 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘮𝘦𝘯𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘪𝘯𝘨 𝘩𝘪𝘴 𝘱𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦 𝘰𝘯 𝘸𝘩𝘦𝘳𝘦 𝘈𝘐 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘩𝘦𝘢𝘥𝘦𝘥 𝘢𝘯𝘥 @Coffee_and_NLP 𝘧𝘰𝘳 𝘵𝘩𝘪𝘴 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸. youtu.be/ea3EPz-hizc?si=g2ML…