ApertureData Secures $8.25M Seed Funding for Innovative Multimodal AI Database

ApertureData offers 10x speed boost to enterprises using multimodal data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Data is the holy grail of AI. From nimble startups to global conglomerates, organizations everywhere are pouring billions of dollars to mobilize datasets for highly performant AI applications and systems. But, even after all the effort, the reality is accessing and utilizing data from different sources and across various modalities -- whether text, video, or audio -- is far from seamless. The effort involves different layers of work and integrations, which often leads to delays and missed business opportunities. Enter California-based ApertureData. To tackle this challenge, the startup has developed a unified data layer, ApertureDB, that merges the power of graph and vector databases with multimodal data management. This helps AI and data teams bring their applications to market much faster than traditionally possible. Today, ApertureData announced $8.25 million in seed funding alongside the launch of a cloud-native version of their graph-vector database. "ApertureDB can cut data infrastructure and dataset preparation times by 6-12 months, offering incredible value to CTOs and CDOs who are now expected to define a strategy for successful AI deployment in an extremely volatile environment with conflicting data requirements," Vishakha Gupta, the founder and CEO of ApertureData, tells VentureBeat. She noted the offering can increase the productivity of data science and ML teams building multimodal AI by ten-fold on an average. What does ApertureData bring to the table? Many organizations find managing their growing pile of multimodal data -- terabytes of text, images, audio, and video daily -- to be a bottleneck in leveraging AI for performance gains. The problem isn't the lack of data (the volume of unstructured data has only been growing) but the fragmented ecosystem of tools required to put it into advanced AI. Currently, teams have to ingest data from different sources and store it in cloud buckets - with continuously evolving metadata in files or databases. Then, they have to write bespoke scripts to search, fetch or maybe do some preprocessing on the information. Once the initial work is done, they have to loop in graph databases and vector search and classification capabilities to deliver the planned generative AI experience. This complicates the setup, leaving teams struggling with significant integration and management tasks and ultimately delaying projects by several months. "Enterprises expect their data layer to let them manage different modalities of data, prepare data easily for ML, be easy for dataset management, manage annotations, track model information, and let them search and visualize data using multimodal searches. Sadly their current choice to achieve each of those requirements is a manually integrated solution where they have to bring together cloud stores, databases, labels in various formats, finicky (vision) processing libraries, and vector databases, to transfer multimodal data input to meaningful AI or analytics output," Gupta, who first saw glimpses of this problem when working with vision data at Intel, explained. Prompted by this challenge, she teamed up with Luis Remis, a fellow research scientist at Intel Labs, and started ApertureData to build a data layer that could handle all the data tasks related to multimodal AI in one place. The resulting product, ApertureDB, today allows enterprises to centralize all relevant datasets - including large images, videos, documents, embeddings, and their associated metadata - for efficient retrieval and query handling. It stores the data, giving a uniform view of the schema to the users, and then provides knowledge graph and vector search capabilities for downstream use across the AI pipeline, be it for building a chatbot or a search system. "Through 100s of conversations, we learned we need a database that not only understands the complexity of multimodal data management but also understands AI requirements to make it easy for AI teams to adopt and deploy in production. That's what we have built with ApertureDB," Gupta added. How is it different from what's in the market? While there are plenty of AI-focused databases in the market, ApertureData hopes to create a niche for itself by offering a unified product that natively stores and recognizes multimodal data and easily blends the power of knowledge graphs with fast multimodal vector search for AI use cases. Users can easily store and delve into the relationships between their datasets and then use AI frameworks and tools of choice for targeted applications. "Our true competition is a data platform built in-house with a combination of data tools like a relational / graph database, cloud storage, data processing libraries, vector database, and in-house scripts or visualization tools for transforming different modalities of data into useful insights. Incumbents we typically replace are databases like Postgres, Weaviate, Qdrant, Milvus, Pinecone, MongoDB, or Neo4j- but in the context of multimodal or generative AI use cases," Gupta emphasized. ApertureData claims its database, in its current form, can easily increase the productivity of data science and AI teams by an average of 10x. It can prove as much as 35 times faster than disparate solutions at mobilizing multimodal datasets. Meanwhile, in terms of vector search and classification specifically, it is 2-4x faster than existing open-source vector databases in the market. The CEO did not share the exact names of customers but pointed out that they have secured deployments from select Fortune 100 customers, including a major retailer in home furnishings, a large manufacturer and some biotech, retail and emerging gen AI startups. "Across our deployments, the common benefits we hear from our customers are productivity, scalability and performance," she said, noting that the company saved $2 million for one of its customers. As the next step, it plans to continue this work by expanding the new cloud platform to accommodate the emerging classes of AI applications, focusing on ecosystem integrations to deliver a seamless experience to users and extending partner deployments.

ApertureData raises $8.5M for multimodal AI database - SiliconANGLE

California-based ApertureData Inc., the developer of a purpose-built database for artificial intelligence multimodal large language models, today announced it raised $8.25 million in a seed round led by TQ Ventures with participation from Westwave Capital and Interwoven Ventures. As more AI-driven applications expand beyond pure text generation and answers, many have become multimodal, this includes text, images, audio, video and sometimes complex nonstructured data. Although these data types can be processed individually with ease, they often live in different databases throughout the enterprise information technology stack and are accessed through varied pipelines. This makes approaches that use them difficult and tedious to build. ApertureData's solution ApertureDB is one database to handle all these datasets in one place. It works with text, images and videos, and can manage unstructured blobs of arbitrary size and complexity. It can store the metadata, unstructured data, and support data all in one place so that it can be fed directly into the AI model without the need for it to be pulled from multiple sources and pipelines for processing. "The increasing adoption of multimodal data in powering advanced AI experiences, including multimodal chatbots and computer vision systems, has created a significant market opportunity," said Vishakha Gupta, chief executive of ApertureData. Numerous companies have rolled out or enhanced multimodal AI models, including Google LLC with Gemini Pro and OpenAI with GPT-4o. Multimodal data fits into many industries, for example, healthcare where doctors need to review CT scans or X-rays, as well as blood tests to diagnose patients and prepare treatment plans. They also take audio notes using dictation, get data from EKG machines and images of written forms. These various formats are unstructured and that cannot be easily represented in spreadsheets or relational databases without transformation and are not simple to search through. However, they underly the fundamental relationships of human interaction with the world. By streamlining the separate processes of storing, transforming and preparing the data into one place, ApertureData says it can reduce the time data scientists and AI engineers need to spend on infrastructure. This can accelerate development time from months to days. According to internal metrics, the company said that ApertureDB is 35x faster than existing siloed solutions at preparing multimodal datasets and 2-4x faster than other open-source vector databases. Alongside the funding, the company announced the launch of ApertureDB Cloud, a fully managed cloud-based unified solution that allows businesses to centralize all these datasets in one place. The service is available for AWS and Google Cloud, with Microsoft Azure available for custom configurations. The multimodal AI market is growing rapidly, according to data from market insights firm MarketsAndMarkets and is projected to grow to $4.5 billion by 2028 from $1.0 billion in 2023. Some key influences include advancements in AI and machine learning, demand for improved user experience and the increased integration of AI into consumer and enterprise applications such as its use in healthcare and other industries. The company said it would use the funds to scale its current production deployments and enhance user experience by improving documentation and sandbox environments. It also intends to broaden its ecosystem integrations and expand its sales and marketing efforts to follow the wave of multimodal AI-driven apps being built.