Swift Inference provides edge‑based AI inference for voice, vision, and large language models, enabling sub‑100 ms response times at telecom scale.
Funding
Funding not disclosed
Founders
Product
Problem
Deploying AI models for voice, vision, and large language tasks through centralized cloud services introduces high latency, excessive bandwidth consumption, and difficulty meeting strict uptime requirements, especially for real‑time applications at telecom scale.
Solution
Swift Inference offers an edge‑computing platform that hosts AI inference workloads on a global network of more than 200 edge nodes located in major metropolitan areas. By placing models close to end users, the service delivers sub‑100 ms response times and up to 17× lower latency compared to traditional cloud providers. The platform automatically routes requests to the nearest node, monitors performance in real time, and scales resources as demand grows, ensuring consistent availability while reducing upstream bandwidth usage.
Target Audience
Primary customers are telecom operators, cloud service providers, and enterprises that require low‑latency, high‑availability AI inference for real‑time voice, video, and conversational applications.
Features
- Global edge network of 200+ locations optimized for telecom‑grade connectivity
- Support for a wide range of AI models, including speech recognition, computer vision, and large language models
- Real‑time performance dashboard with latency, throughput, and error monitoring
- Automatic scaling and load balancing across edge nodes to maintain SLA compliance
- Integrated bandwidth optimization that routes inference traffic locally, minimizing backhaul usage
- Simple three‑step deployment workflow: select model, choose edge location, activate inference in minutes