Pienso recently raised $10 million in a Series A funding round led by Latimer Ventures with participation from Gideon Capital, SRI, Uncork and Good Growth Capital. It’s a business model that appeals to investors, apparently. “We wanted to give customers the freedom to experiment with building new models before deploying them.” “We intentionally designed our pricing to allow customers to test out models beforehand to understand how AI can help them without first having to make a sizable investment,” Jones added. The greater the number of models, the higher the licensing cost. “This alleviates the privacy concerns of using … models, and also is more accurate, capturing the nuances of each individual company.”Ĭompanies pay Pienso a yearly license based on the number of AI models they deploy. “Pienso’s flexible, no-code interface allows teams to train models directly using their own company’s data,” Jones said. government agency has tested it to monitor illegal weapons tracking. broadcaster, is using Pienso to analyze customer service calls, Jones says, while an unnamed U.S. But it can also operate without APIs or third-party services, keeping data within a secure environment. (It depends on the model, but AI generally needs labels - like an image of a bird paired with the label “finch” - to learn to perform a task.) The platform, which can be deployed in the cloud or on-premises, integrates with enterprise systems through APIs. Pienso guides users through the process of annotating or labeling training data for pre-tuned open source or custom AI models. Pienso believes that any domain expert, not just an AI engineer, should be able to do just that.” To be able to scale to AI’s full potential, where it can manage business processes and interact with customers, you have to be able to train and fine-tune your model. “So much of the AI conversation has been dominated by … large language models,” Jones said, “but the reality is that no one model can do everything. What resulted was Pienso, which Jones describes as an AI suite built for “non-technical talent” - namely researchers, marketers and customer support teams who have access to large amounts of data for AI training, but lack the necessary resources to structure and analyze it. They built tools for this purpose, and some years later, Jones and Dinakar teamed up to commercialize these tools. Jones and Dinakar eventually realized that the solution was to have subject-matter experts - in this case, teenagers - help train the model. “There was just one problem: While the model itself worked the way it was supposed to, it wasn’t trained on the right data, so it wasn’t able to identify harmful content that used teenage slang.” “We teamed up for a class project to build a tool that would help social media platforms moderate and flag bullying content,” Jones, who serves as Pienso’s CEO, told TechCrunch in an interview. The two met a few years back at MIT’s Media Lab as graduate students. (See ML Hub, Kore.ai and Viso, to name a few.) One of the newer entrants is Pienso, a platform that lets users build and deploy models without having to write code.īirago Jones and Karthik Dinakar founded Pienso in 2016 based on their research at MIT (they’re alumni). Of businesses responding to the S&P poll, around half said they aren’t ready to implement AI - and won’t be for five years or more.įortunately, there’s an increasing number of products from startups and Big Tech vendors alike that aim to tackle these AI deployment roadblocks. The reasons for the slow ramp-ups vary, but the commonly cited ones are challenges around data management, security and compute resources. But that doesn’t mean it’s getting easier to deploy.Īccording to a 2023 S&P Global survey, about half of companies with at least one AI project in production are still at the pilot or proof-of-concept stages. AI might be the “it thing” of the moment.
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