Chips are only the foundation; software optimization is the key. We work closely with chip vendors and optimize extensively from the instruction set to the operator layer. We also avoid generic libraries for data processing, using the chip SDK directly for raw chip acceleration—so the same chip delivers higher efficiency. It's like the same engine: different tuning produces different horsepower.

Xeroptix prioritizes on‑premise analysis and edge‑side processing to minimize raw‑data egress. In scenarios requiring networked collaboration, we reduce risk through access control, transport encryption, and project‑level isolation.

Before optimizing, we define business metrics and acceptance criteria, then perform model compression, inference acceleration, and threshold tuning. The goal is more stable real‑time performance within an acceptable accuracy range.

Yes. XClaw is designed for iterative delivery and supports continuous adjustment of algorithms, rules, alerting policies, and workflows—ideal for gradual expansion across scenarios.

We can assess adaptation based on model architecture, data type, and deployment hardware. We first run compatibility and performance tests, then provide deployment recommendations.

Usually no large‑scale modification of on‑site systems is needed. We proceed through device onboarding, algorithm configuration, integration testing, and operations handover to keep go‑live complexity as low as possible.

Each project clearly defines response levels, handling processes, and delivery boundaries. After go‑live we provide remote troubleshooting, version updates, and on‑site support as needed to keep critical operations running.

Premium services typically cover custom models, complex‑scenario tuning, multi‑site deployment management, data closed‑loop, and operations assurance—helping customers move vision AI from single‑point trials to stable, large‑scale use.