Traini’s Sentra Smart Collar Ranked #1 AI Hardware Product at CES 2026; Company Secures $7.5M to Expand Pet Emotion AI Platform

Traini

Company Announces Funding Round Following CES Recognition for Its Cognitive Sensing Node for Dogs

PALO ALTO, Calif., Feb. 20, 2026 (GLOBE NEWSWIRE) -- Traini today announced that its Sentra smart collar was ranked the No. 1 AI Hardware Product at the Consumer Electronics Show 2026 (CES 2026) in a published list of the Top 30 Most Noteworthy AI Hardware Products. The recognition follows the company’s recent completion of a $7.5 million financing round to accelerate product deployment and platform expansion.

Sentra is designed as a cognitive sensing node for dogs, powered by Traini’s proprietary Pet Emotional & Behavioral Intelligence (PEBI) platform. The device integrates multimodal AI models to analyze canine vocalizations, posture, movement, physiological signals, and environmental context in real time.

According to Traini, the PEBI system currently supports nearly 120 dog breeds and achieves up to 94% accuracy in core emotion-translation tasks under validated testing conditions.

Product Overview: Sentra Cognitive Collar

Sentra combines hardware sensing with Traini’s on-device AI platform to deliver:

Real-time talking and emotional state analysis
Behavioral intention modeling
Continuous health trend monitoring
Location-aware contextual insights

The collar embeds Traini’s Valence–Arousal (VA) emotional modeling framework, which maps canine emotional states into a continuous coordinate system rather than discrete labels. The system analyzes bark acoustics, heart rate variability, body temperature, posture, and movement patterns to generate interpretable outputs for pet owners.

Traini

Technology Platform: Pet Emotional & Behavioral Intelligence (PEBI)

Traini’s PEBI platform integrates four core technical layers:

Semantic Interface Layer – Translates multimodal behavioral signals into structured emotional and behavioral representations.
Multimodal Perception Layer – Combines vision, audio, physiological telemetry, environmental data, and spatial movement.
State Reasoning & Prediction Layer – Uses continuous emotion modeling and behavioral history to assess emotional state, intent, and potential health risk indicators.
Expression & Visualization Layer – Converts model outputs into user-facing insights, including 3D emotional state tracking.

The company states that its dataset includes real-world multimodal data collected from over two million dogs, with annotation performed by professionals with veterinary and animal behavior backgrounds. Traini also operates a continuous model improvement framework, in which anonymized real-world usage data is selectively integrated into retraining pipelines.

Funding and Expansion Plans

The recently completed $7.5 million financing round will be used to:

Scale manufacturing of Sentra smart collars
Expand clinical and veterinary partnerships
Develop API integrations for pet clinics and hardware manufacturers
Advance personalization capabilities within the PEBI platform

Traini also confirmed ongoing development of AI agents built on top of its core model stack, including training guidance systems, health management modules, and personalization engines.

Market Context

The global pet wearable and pet health monitoring market has grown steadily in recent years, driven by increasing demand for preventive care and connected pet technologies. Traini’s positioning focuses on multimodal AI interpretation of emotional and behavioral signals, rather than solely activity tracking.

Sentra is currently available for reservation via Traini’s official website https://traini.app/collar-buy

About Traini

Traini is a technology company developing multimodal AI systems for pet emotional and behavioral understanding. Its Pet Emotional & Behavioral Intelligence (PEBI) platform integrates perception models, reasoning systems, and intelligent agents to support training, health monitoring, and human–pet interaction.

Media Contact:

Arvin

trainipet@gmail.com

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Traini

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