Ensemble Secures $3.3M to Address Enterprise AI's 'Dark Matter' Data Challenge

Ensemble raises $3.3M to bring 'dark matter' tech to enterprise AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Machine learning startup Ensemble has raised $3.3 million in seed funding to address the growing importance of data quality in artificial intelligence. Salesforce Ventures led the round, with participation from M13, Motivate, and Amplo. Founders Alex Reneau and Zach Albertson are pioneering a novel approach to data representation that promises to enhance machine learning model performance without requiring vast amounts of additional data or complex model architectures. Unlocking hidden data relationships with 'dark matter' technology "We have a new way to essentially approximate hidden relationships in your data or missing information that you wish was originally in your dataset to improve your model," said Alex Reneau, CEO of Ensemble, in an exclusive interview with VentureBeat. "We're able to enable customers to maximize their own data that they're working with, even when it's limited, sparse, or highly complex, allowing them to train effective models with less comprehensive information." The company's proprietary "dark matter" technology slots into the machine learning pipeline between feature engineering and model training. It creates enriched data representations that can uncover latent patterns and relationships, potentially making previously unsolvable problems tractable. Addressing enterprise AI adoption challenges This approach comes at a critical time for enterprise AI adoption. Despite rapid advances in AI capabilities, many organizations struggle to deploy models in production environments due to data quality issues. Caroline Fiegel, an investor at Salesforce Ventures, explained the rationale behind their investment: "We have maybe watched over the past 12 to 24 months, enterprises move more slowly into AI and into production than we had anticipated," she told VenutreBeat. "When you peel that back and really start to understand why, it's because the data is disparate. It's kind of low quality. It's riddled with PII." Ensemble's technology could have far-reaching implications across industries. The company is already working with customers in biotechnology and advertising technology, with early results showing promise in areas such as predicting virus-host interactions in the gut microbiome. From impossible to possible: Expanding the horizons of machine learning "We actually care a lot more about the cases where ML is able to do what was otherwise impossible before," Reneau emphasized. "So it's not just about doing what a human can do, and making it faster, but [it's about] what a human couldn't do." The funding will be used to accelerate product development, expand the team, and ramp up go-to-market efforts. As the AI landscape continues to evolve rapidly, Ensemble sees its role as providing a foundational technology that can adapt to changing needs. "With these models constantly developing, and the data landscape is going to be ever-evolving, I think that we're definitely more set -- on the core research side of it," Reneau said, hinting at the company's long-term vision. For Salesforce Ventures, the investment aligns with their thesis on the critical role of data in AI adoption. "Building trust in AI today is really built in outcomes," Fiegel said, "and so knowing that Alex and Zach kind of share that core north star with us is what keeps us excited." As enterprises grapple with the challenges of implementing AI at scale, Ensemble's approach to data quality could prove to be a key enabler. The company's progress will be closely watched by both the tech industry and the broader business community as a potential solution to one of AI's most persistent obstacles.

Startup Ensemble gets $3.3M in funding to fix data quality issues with Dark Matter - SiliconANGLE

Startup Ensemble gets $3.3M in funding to fix data quality issues with Dark Matter Ensemble AI Inc. is looking to tackle headaches around data quality and help companies build more powerful artificial intelligence models after closing on a $3.3 million seed funding round, it said today. The round was led by Salesforce Ventures, with Amplo, M13 and Motivate also participating. They're backing Ensemble because the startup has created a pioneering approach to data representation in order to enhance the performance of AI models, without pumping them with vast amounts of extra data or creating more complicated model architectures. What the startup is doing is using machine learning techniques to enhance AI models, by helping them uncover hidden relationships between their datasets. The company explains that if AI is going to be able to solve real-world problems, it needs access to more, and better equality data. Many companies struggle with limited and sparse or one-dimensional datasets, and that prevents their AI models from generating meaningful or useful results. Data scientists spend hours trying to fix their data to overcome this, and some progress has been made with more sophisticated AI model architectures, but such endeavors require vast resources and technical expertise that not every company has. To solve these issues, Ensemble has created a novel embedding model it calls Dark Matter, which uses an "objective function" to create richer representations of data for predictive tasks. Dark Matter is said to be able to understand the complex, non-linear relationships within datasets through a lightweight data transformation. It distills the complexity of these relationships into a simple "data representation", so engineers can build better quality AI models that can tackle much harder problems. Ensemble co-founder and Chief Executive Alex Reneau explained that Dark Matter slots in between the feature engineering and model training and inference processes within data pipelines. "We're able to enable customers to maximize their own data that they're working with, even when it's limited, sparse, or highly complex, allowing them to train effective models with less comprehensive information," he said. "This foundational technology frees up data scientists to focus on experimentation and also makes ML viable for problems previously unable to be modeled, unlocking new capabilities for our customers." The startup believes Dark Matter is a superior solution compared to synthetic data, which is often used by AI developers to compensate for low-quality or sparse datasets. It explains that while Dark matter does create new variables, the mechanics are fundamentally different. Because synthetic data recreates existing distributions from Gaussian noise, it means that no new information is actually created. The synthetic data merely mirrors the statistical properties of the existing data, and so there's no meaningful impact on predictive accuracy, the company explained. On the other hand, Dark Matter learns how to create new embeddings with fundamentally different statistical properties and distributions that result in measurable improved predictive accuracy. Salesforce Ventures' Caroline Fiegel told VentureBeat that Ensemble offers a promising solution that can potentially accelerate the adoption of AI. She explained that many organizations are struggling to deploy AI models in production due to issues with poor data quality. "When you peel that back and really start to understand why, it's because the data is disparate. It's kind of low quality," she said. "It's riddled with PII." Ensemble says Dark Matter has already been put to use by a number of early adopters in areas such as biotechnology, healthcare, personalization and advertising technology, with promising results. For instance, one biotech customer has used its tech to create a model that's better able to predict virus-host interactions within the gut microbiome, it said. Looking forward, Ensemble said it will use the funds from today's round to expand its team and accelerate its product development and go-to-market plan.