Carlinx Flow-C: Cost Reduction Starts with the Data Foundation
At the 2026 Beijing International Automotive Exhibition, Carlinx officially launched Flow-C, a next-generation vehicle-cloud integration data acquisition solution designed for AIDV (AI-Defined Vehicle).
Flow-C integrates the unified messaging kernel of EMQ’s FlowMQ with Carlinx’s HyperFlow data acquisition capabilities. Built on three core innovations -cloud-native stateless architecture, low-cost persistent storage via object storage, and edge-based processing - it overhauls the entire automotive lifecycle data acquisition pipeline. Flow-C addresses long-standing challenges: high infrastructure costs, fragmented data silos, and complex operations. It helps OEMs push infrastructure costs furtherto their practical limits in large-scale operations, providing a cost-effective, highly reliable technical foundation for software-defined vehicles(SDV) and AI-driven data loops.
Industry Pain Point: Coarse Data Acquisition is Limiting AI Deployment in Vehicles
The automotive data acquisition industry currently faces a structural dilemma: high data transmission costs, high storage costs, high operational complexity, and high integration costs. Protocol barriers have created data silos, leading to inefficient AI-data loops. As a result, large language models (LLM)struggle to access real-time, complete, cross-domain vehicle data.
Over the past decade, separate systems havebeen deployed for different scenarios, resulting in heterogeneous silos. Integration between systems relies on “bridging,” which introduces latency and potential failure points. Cloud storage, heavily dependent on expensive block storage clusters, scales poorly and incurs rigid costs. This natural evolution of messaging infrastructure has led to “protocol fragmentation” and a proliferation of “chimney” deployments.
Rebuilding the Architecture from the Ground Up: Cost Reduction at the Source
1. Decouple Storage and Compute - Eliminate Redundancy
Flow C adopts a stateless, cloud-native architecture. Compute nodes handle protocol processing, while data is persisted in low-cost object storage (e.g., S3/OSS). This design eliminates the need for automakers to provision expensive physical servers or block storage to handle peak traffic. Compute and storage resources can scale independently in seconds based on real-time load, improving resource utilization by over 40%.
2. Write Once, Use Many Times
Under Flow C, a single stream of data uploaded from a vehicle can be consumed simultaneously by three modes: Pub/Sub, queue, and stream processing. This eliminates duplicate acquisition, transmission, and storage caused by different business needs, maximizing data value across its lifecycle.
3. Edge Pre-Processing - Lighten the Load
By integrating Hyper Flow’s edge computing capabilities, Flow C pushes data acquisition logic to the edge. Vehicles pre-filter, compress, and run rule-based calculations on data locally; only “valuable” data is sent to the cloud. This dramatically reduces expensive cloud storage and computational pressure.
Data Infrastructure for the AI Era
As automotive intelligence enters the “agent-driven” phase, data infrastructure must provide AI agents with a unified, real-time, low‑cost context environment. Flow C’s architecture - multi-protocol unification + object storage + edge intelligence - is purpose-built for this. Heterogeneous system data converges through a unified bus; terabytes or petabytes of historical data are stored cost-effectively for model training; real-time streaming data reaches an inference engine in milliseconds.
Going forward, Carlinx will continue deepening its collaboration with EMQ. With a focus on unified messaging, minimalist architecture, and extreme cost efficiency, we aim to help automakers break down data barriers, unlock data value, and accelerate the deployment of AI data loops for smart vehicles. Together, we will drive the automotive industry toward a more efficient, cost‑effective, and intelligent future - making every mile more valuable.
