- Pienso, founded in 2016 by Birago Jones and Karthik Dinakar from MIT, offers no-code AI model training tools.
- Challenges in AI deployment persist despite its growing popularity, with many companies still in the pilot stages.
- Pienso addresses hurdles like data management and security, allowing non-technical users to build and deploy AI models.
- The platform integrates with enterprise systems via APIs and offers both cloud and on-premises deployment options.
- Notable users include Sky and a U.S. government agency for analyzing customer service calls and illegal weapons tracking, respectively.
- Pienso operates on a subscription-based model, enabling customers to experiment before committing.
- Recent Series A funding of $10 million signals investor confidence in Pienso’s approach to democratizing AI.
Main AI News:
In today’s business landscape, the prominence of AI is undeniable. However, despite its buzz, deploying AI remains a challenge. A recent survey by S&P Global in 2023 revealed that nearly half of the companies with AI projects in production are still in the pilot or proof-of-concept stages. The hurdles to widespread adoption are multifaceted, ranging from data management and security to computing resource constraints.
In response to these challenges, an array of products from both startups and established tech giants has emerged to address the bottlenecks in AI deployment. Among these innovators is Pienso, a platform founded by Birago Jones and Karthik Dinakar in 2016, stemming from their research at MIT. Their platform enables users to construct and implement AI models without the necessity for coding expertise.
Jones and Dinakar’s journey began during their time at MIT’s Media Lab as graduate students. Their collaboration on a class project aimed at developing a tool to assist social media platforms in identifying and flagging bullying content unveiled a crucial insight. Despite the model’s functionality, it failed to recognize harmful content using teenage slang due to inadequate training data. Realizing the need for subject-matter experts to refine the model, they devised tools for this purpose, laying the groundwork for Pienso’s creation.
Pienso serves as an AI suite tailored for non-technical professionals, including researchers, marketers, and customer support teams. These individuals often possess vast datasets crucial for AI training but lack the resources for data structuring and analysis. The platform facilitates the annotation and labeling of training data essential for refining open-source or custom AI models. Integrating seamlessly with enterprise systems via APIs, Pienso ensures data security by enabling both cloud-based and on-premises deployment.
Notable adopters of Pienso include Sky, the U.K. broadcaster, utilizing it to analyze customer service calls, and an undisclosed U.S. government agency employing it for illegal weapons tracking. Pienso’s no-code interface empowers teams to train models directly with proprietary data, addressing privacy concerns and enhancing accuracy.
Pienso operates on a subscription-based pricing model, allowing customers to experiment with various AI models before committing to deployment. Recently securing $10 million in Series A funding led by Latimer Ventures, with participation from Gideon Capital, SRI, Uncork, and Good Growth Capital, Pienso is poised for expansion. The influx of capital will fuel the scaling of sales, marketing, and customer success initiatives, as well as facilitate talent acquisition and platform enhancement.
According to Luke Cooper of Latimer Ventures, Pienso’s approach resonates with investors due to its focus on democratizing AI. By empowering domain experts to leverage their data effectively, Pienso is paving the way for smarter AI models tailored to specific applications, heralding a future where problem solvers drive innovation in AI.
Conclusion:
Pienso’s innovative no-code approach to AI deployment presents a significant shift in the market. By empowering non-technical users to harness AI capabilities, Pienso addresses longstanding challenges in adoption. This democratization of AI not only enhances accessibility but also fosters innovation, heralding a future where domain experts drive the evolution of AI applications across industries.
