Spiral

Spiral operates as a specialized intelligence platform for modern customer support and experience teams, aiming to convert qualitative customer feedback into a strategic asset. Founded in Seattle in 2018 by former Amazon software engineers Elena Zhizhimontova and Andrew DiLosa, the company was born from their direct experience with the challenges of managing vast amounts of customer feedback data at scale. Their time on the Fire TV team at Amazon highlighted the difficulty companies face in extracting actionable insights from the sheer volume of call recordings, chat transcripts, and survey responses they collect.

The company's core offering is an AI-powered platform that autonomously aggregates and analyzes omnichannel customer feedback. By integrating with a client's existing CRM systems, contact centers, and other feedback sources, Spiral's technology scans thousands of communications to identify, classify, and track customer issues without manual data tagging. This process allows businesses to move beyond reactive problem-solving by uncovering emerging trends, pinpointing root causes of customer friction, and understanding the operational impact of these issues. The platform provides insights through a unified dashboard, enabling support, product, and CX teams to proactively address problems before they escalate, thereby improving customer retention and operational efficiency.

Spiral's business model is structured around a Software-as-a-Service (SaaS) subscription, targeting medium-to-large enterprises in sectors such as banking, fintech, and connected devices. The company has secured a total of $6.9 million in funding over several rounds, with notable investors including Trilogy Equity Partners, Bezos Expeditions, and Techstars. This capital has supported the platform's development, including its ability to analyze voice calls and expand its integration capabilities. Having participated in the Amazon Alexa Accelerator, Spiral leverages large language models (LLMs) to offer what it positions as an enterprise-grade AI analysis layer at a lower cost than building an in-house data science team.

Keywords: customer feedback analytics, support intelligence, CX analytics, voice of the customer, AI for customer service, unstructured data analysis, root cause analysis, issue tracking, customer retention, experience management, support operations, call transcript analysis, chat log analysis, survey data mining, product feedback management, SaaS, B2B, support ticket classification, customer journey analytics, agent performance