Synaptics is working with fellow Californian company Righ on smart-home agentic AI applications.

Righ’s distributed agentic AI platform RighValor now runs on the Synaptics Astra SL1600 family of systems-on-chip (SoCs), delivering privacy-first, real-time intelligence at the edge.

Righ is a pioneer in distributed agentic AI at the edge. It is partnering with Synaptics for smart-home agentic AI applications.

RighValor is a distributed agentic AI platform built for privacy, performance and customisation, and marks a step forward in how homes process and act on data.

Together, Righ and Synaptics are delivering live, on edge agentic AI powered by the Astra SL1680 SoC and RighGravity (www.righ.com/righgravity), Righ’s network control plane and data management service. This combination enables intelligent services to run directly in the home without relying on cloud compute, reducing latency, ensuring privacy and lowering operational costs.

“Righ was created to rethink how technology works in the home,” said Miro Samardzija, chief product officer at Righ (www.righ.com). “With Synaptics, we’re proving AI can be intelligent, privacy-first and cost-efficient, creating new revenue for partners. We’re proud to be Synaptics’ first smart-home agentic AI partner.”

Siddarth Chandrasekar, senior director at Synaptics (www.synaptics.com), added: “At Synaptics, we’re enabling powerful AI capabilities where they matter most at the device edge. Righ’s distributed AI architecture complements Synaptics’ Astra family of processors. We’re excited to showcase what’s possible when advanced hardware meets robust software. This partnership will enable a range of real-world applications for the smart-home industry.”

At next week’s IBC (show.ibc.org) show in Amsterdam, Righ and Synaptics will show a live deployment of RighValor (www.righ.com/righvalor) running on Astra SL1680 (www.synaptics.com/assets/product-brief/astra-sl1680-embedded-iot-processor) inside Synaptics’ invite-only demo suite. The demonstration will highlight:

• Distributed parallelism: Optimises speed, efficiency and localised intelligence by distributing tasks across devices in the home
• Device identification: Recognises users and devices in real time for personalised automation, access control and cyber security
• On-device image processing: Captures and processes visuals locally for faster response and stronger privacy

This capability allows service providers and device manufacturers to deploy AI services with zero reliance on cloud compute, deliver premium customised user experiences, improve privacy and speed while reducing infrastructure costs, and increase ARPU through automation and intelligent value-added services.