SoundHound AI announced the launch of its Sales Assist voice‑powered AI agent, a real‑time agentic solution designed for retail sales floors, and will demonstrate the technology at Mobile World Congress 2026 in Barcelona.
The agent builds on SoundHound’s Polaris foundation model and next‑generation agentic AI platform, orchestrating multiple specialized agents that securely access CRM, billing, and product databases to deliver instant, data‑driven recommendations to floor associates. The goal is to shorten sales cycles, reduce training time, and increase revenue per customer in noisy retail environments.
The retail agentic‑AI market is projected to reach $60.43 billion in 2026 and grow to $218.37 billion by 2031, making this launch a strategic entry into a high‑growth segment. SoundHound’s Polaris model has outperformed Google and OpenAI in accuracy and latency, giving it a competitive edge over players such as Google, Amazon, C3.ai, and Cerence.
SoundHound reported Q2 2025 revenue of $42.7 million, up 217% year‑over‑year, and Q3 2025 revenue of $42 million, up 68% year‑over‑year, while maintaining an adjusted EBITDA loss of $14.3 million in Q2 and $14.5 million in Q3. The company’s cash position was $268.9 million as of September 30 2025, with no debt, positioning it to invest in new product development.
The Sales Assist agent is in early deployment, with pilot programs underway in select retail and telecommunications environments. While specific customer adoption metrics are not yet disclosed, the launch signals SoundHound’s intent to capture a share of the agentic‑AI market and diversify beyond its core voice‑recognition offerings. The upcoming Q4 2025 earnings report on February 26 will provide further insight into revenue performance and guidance.
The introduction of Sales Assist at MWC 2026 underscores SoundHound’s commitment to expanding its enterprise AI portfolio and leveraging its proprietary Polaris technology to address real‑world retail challenges, positioning the company for growth in a rapidly expanding agentic‑AI landscape.
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