Sierra Ventures: Our Early-Stage Investment in Siena AI

Sierra Ventures: Our Early-Stage Investment in Siena AI

Written by

Tim Guleri

Published on

February 22, 2024

We are excited to announce that Sierra Ventures has led NY-based Siena’s 4.7M Seed round. Led by visionary founders Andrei Negrau and Lisa Popovivi, Siena is redefining customer service for e-commerce merchants using a Generative AI native approach.  

 

Sierra Ventures has a long history of creating success and investing in Customer Service Automation.  Sierra partners held leadership positions in Scopus (NASDAQ:  SCOP), Octane Software (3.2B exit to Epiphany (NASDAQ:  EPNY), and more recent investments in Supportlogic and Blato.  We were on the hunt for a vertically focused Generative AI company with self-learning and multi-modal capabilities, and Andrei and Lisa had built something really impressive. 

 

Siena has built a vertically integrated autonomous customer service platform, which has received very well in the market. Since launching nine months ago, Siena has already closed 80+ customers around the world, responds in 100+ languages natively, processed over 1 million conversations, saved 20 thousand hours in support (or 833 days), and does it across all channels, including email, social media comments, DM’s, website and text.

 

Our investment thesis was anchored in 3 main areas: 

 

  1. Generative AI has forced us to completely rethink software's positive impact on service-heavy sectors, particularly the $41B Customer Support Market and the $525B BPO Market. The influx of unstructured data and the dependence on manual processes have long been pain points in these industries, making them ripe to the benefits of large language models (LLM’s) and generative AI applications. The potential benefits of generative AI in these markets are vast. It's not just about cost reduction; it's about transforming the entire landscape of customer support and BPO by providing more efficient, accurate, and scalable solutions.

 

  1. We believe the next wave of Vertical SaaS must be AI native. The combination of purpose-built UX, deep integrations across an ecosystem of tools and industry-specific LLM’s can unlock unprecedented efficiencies, workflows, and customer value. This approach represents a significant evolution in how software solutions cater to diverse sectors. These LLMs, trained on sector-specific data, can comprehend industry nuances, enabling software to provide highly relevant intelligence, insights, recommendations, and predictive functionalities. Deep integrations across an ecosystem of tools further enhance the usability and effectiveness of these AI-native SaaS products. Furthermore, the emphasis on domain-specific and purpose-built user experiences ensures that these solutions are both technologically advanced and intuitive and user-friendly. Siena exemplifies this approach: their AI model and robust e-commerce integrations not only is designed to cut costs but also open up new revenue streams. For one of their clients, Siena saves over 80 hours weekly in customer service time and boosted Gross Merchandise Value (GMV) sales by an extra $50-100k per quarter. 

 

  1. Siena has developed a product designed to go beyond the inherent advantages of LLMs. Their system optimizes AI agents' performance using a closed loop, dynamically improving the AI model based on human feedback and customer queries. Moreover, they've constructed an impressive technology stack to efficiently manage integrations, context retrieval, multi-channel communication, and intricate ticket resolutions. This approach boosts personalization in customer responses and drives ticket resolution efficiencies. For their client, Simple Modern, Siena's efforts resulted in a remarkable 98% customer satisfaction rating and an 80% automation rate in support tickets after 60 days of being implemented.

 

We see Siena as a company that can perfectly pair the impact and value of generative AI to an acute problem e-commerce merchants face and Sierra is proud to support them on their journey.