Vishakha Gupta

ApertureDB Multimodal AI Workflows - Automate common AI tasks for multimodal data

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How do you easily generate embeddings, detect objects, infer new attributes, or query your multimodal data? Stop wrestling with your datasets - use ApertureDB Multimodal AI workflows instead! Ingest or enrich complex datasets, run Jupyter notebooks, and more.

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Vishakha Gupta
Hello, Product Hunt Community 👋, I’m Vishakha Gupta, CEO and Co-founder of ApertureData. I am excited to do my very first Product Hunt Launch, for ApertureDB Multimodal AI Workflows. If we want AI to think like humans, it needs all the signals a human brain processes to form our stories, which means multimodal data. Having worked with AI and data for years, I understand the immense challenge users face when trying to leverage multimodal data. Real world data is messy and hard to access. Why? It’s scattered in silos, there are different databases or data tools built based on the modality of data, and how on earth do you talk to all those data types through one language? Teams spend countless hours wrangling images, text, and other data types, trying to build AI applications that truly deliver value. This leads to delayed deployments, increased costs, and missed opportunities. Sounds familiar? 🙄 That’s exactly why we built ApertureDB — a purpose-built database that unifies multimodal data management, vector search, and knowledge graphs into a single backend, delivering up to a 10x productivity boost and accelerating AI deployments by 6-9 months on average. Building on this foundation, we’re excited to launch ApertureDB Multimodal AI Workflows today — the easiest way to manage multimodal datasets with just a few clicks on a scalable cloud platform. 🚀 What are ApertureData Multimodal AI Workflows? Workflows provide a way to automate the execution of a commonly-performed task, when managing multimodal datasets for AI. You can use preconfigured sample sets or your own data when executing the workflows. Here’s a breakdown of the workflows for today’s launch: ✅ Ingest Datasets: Ingest datasets into ApertureDB, from a selection of preconfigured public datasets and explore how it can be used with real data. ✅ Automated Embedding Generation: Generate embeddings for images, then use these embeddings to search for similar images or classify them based on the embeddings. ✅ Detect Faces: Add bounding boxes for faces detected within images in ApertureDB. ✅ Detect Object: Add labelled bounding boxes for objects detected within images in ApertureDB ✅ Direct Jupyter Notebook Access: Run Jupyter notebooks with seamless access to your ApertureDB instance. 🔥 Why ApertureData Multimodal AI Workflows stand out? To deliver a meaningful AI solution built on rich data, today’s infrastructure requires stitching together disparate a number of tools. ApertureDB workflows offer a unified approach. 💸Use Cases From e-commerce, smart retail, to inspection and life sciences, whether you are building classic ML, analytics or Generative AI applications, these workflows empower users to build advanced AI applications with ease. 😍 Want to Try it Out? Sign Up for a free 30-day trial of ApertureDB - https://cloud.aperturedata.io/si... We’d love your feedback — share your thoughts at team@aperturedata.io Have questions or need help – we are available on Slack A special perk for the Product Hunt community: Join our Virtual Lunch and Learn today (March 13th, 2025 @ 9AM PST ) for a deep dive into Workflows. There is still time to Save Your Spot - https://lu.ma/vnabtolp! What’s Coming Next? We’re continuously expanding our workflow library (available to the community on Github) to cover more use cases and integrations. 📢 Soon, you’ll be able to: * Integrate with more data sources and AI models * Launch embedding, graph, and attribute classification for other data types beyond images * Access advanced customization options for specific business needs. Huge thanks to this amazing community — your support is invaluable! Let’s build the future of multimodal AI together. 🚀 Best Regards, Vishakha Gupta CEO, ApertureData
Vishakha Gupta

If you want to see a live demo of how you can use workflows, do join us for our lunch & learn in the morning at 9am PT
https://lu.ma/vnabtolp

@vishakha_gupta4 the prebuilt workflows are great, can teams create their own workflows, or is customization limited? Any API support for integrating with existing ML pipelines?

Gautam Saluja

@vishakha_gupta4 @hamza_afzal_butt The best part is that what the workflows do is open source. While the workflows on the cloud UI are a subset of possibilities, this repository has the all the detailed workings of workflows under the hood.


With this repository as a reference guide, following are the possibilities:

  1. You may refer to what those scripts are doing to get a blue print for building your own workflow.

  2. You may submit a PR. A PR for any custom workflow would be highly encouraged. TIA.

  3. If it is a general enough workflow, it would eventually get published on the cloud UI too!


Vishakha Gupta

@hamza_afzal_butt  do join in the lunch & learn happening now - it's one of the things Luis can answer showing how to from the repo as Gautam described : https://lu.ma/vnabtolp


Michael Vandi

This is a game-changer for AI developers! Congrats on the launch @ApertureDB

Vishakha Gupta

@michael_vandi thanks a lot. We are happy to be working with you all!

David Aronchick

This is the hidden missing piece in SO MANY ML workloads. Great work by the ApetureDB team!

Vishakha Gupta

Thank you @aronchick we look forward to our collaborative examples coming in the near future to demonstrate how everyone can use these end to end even starting from edge to query

Eoin McMillan

Hi Vishakha – How does ApertureDB compare to alternatives in terms of read/write speed and query performance on both small and large datasets? Additionally, does it have any unique optimizations or "special sauce" for faster token processing?

Vishakha Gupta

@mceoin great question - we have some recent benchmarking results summarized here: https://docs.aperturedata.io/category/benchmarks--comparisons

Mainly, for vector search, we are anywhere between 2-10X faster in terms of KNN throughput and offer sub-10msec latencies on service side. For graph search, our prior evaluations against Neo4j put us sometimes over 30X faster. Mainly, ApertureDB continues to scale for very large workloads (Billion scale graphs so far and 10s and millions of embeddings per search space). We have optimizations when we load data - so far we have tested it more on parallel load of large number of blobs or images - we can extend that to faster token processing though we are yet to test it.

Mahima Manik

Love this! Super useful for devs. Congrats on the launch!

Vishakha Gupta

@mahima_manik thank you for your support. Looking forward to integrating this with Datahawk!