Pienso builds no-code tools for training AI models
Reading Time: 3 minutesAI might be the ‘it thing’ of the moment. But that doesn’t mean it’s getting easier to deploy.
According to 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. The reasons for the slow ramp-ups vary, but the commonly cited ones are challenges around data management, security and compute resources.
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.
Fortunately, there’s an increasing number of products from startups and Big Tech vendors alike that aim to tackle these AI deployment roadblocks. (See Google’s 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.
Birago Jones and Karthik Dinakar founded Pienso in 2016 based on their research at MIT (they’re alumni). The two met a few years back at MIT’s Media Lab as graduate students.
Jones and Dinakar eventually realized that the solution was to have subject-matter experts — in this case, teenagers — help train the model. They built tools for this purpose, and some years later, Jones and Dinakar teamed up to commercialize these tools.
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.
‘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. 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. Pienso believes that any domain expert, not just an AI engineer, should be able to do just that.’
Pienso guides users through the process of annotating or labeling training data for pre-tuned open source or custom AI models. (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. But it can also operate without APIs or third-party services, keeping data within a secure environment.
Sky, the U.K. broadcaster, is using Pienso to analyze customer service calls, Jones says, while an unnamed U.S. government agency has tested it to monitor illegal weapons tracking.
‘Pienso’s flexible, no-code interface allows teams to train models directly using their own company’s data,’ Jones said. ‘This alleviates the privacy concerns of using … models, and also is more accurate, capturing the nuances of each individual company.’
Companies pay Pienso a yearly license based on the number of AI models they deploy. The greater the number of models, the higher the licensing cost.
‘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. ‘We wanted to give customers the freedom to experiment with building new models before deploying them.’
It’s a business model that appeals to investors, apparently. 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.
Bringing Pienso’s total raised to $17 million, Jones says that the new cash will be put toward scaling up Pienso’s sales, marketing and customer success teams, recruiting engineering talent and building new features for the platform.
Said Latimer Ventures’ Luke Cooper in a statement: ‘We constantly hear about the need to democratize AI, but what makes Pienso stand out is how they’re thinking about the role of a domain expert in this equation. They’re empowering those who understand their data best to get the most insights out of it. It’s fostering a future where we’re building smarter AI models for a specific application, by the people who are most familiar with the problems they are trying to solve.’
Ref: techcrunch
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