Software-Defined Charging
An AI-native, edge-first EV charging platform built for an industry losing $25–30B annually to preventable failures. 319 microservices, ~20 AI agents, 40+ operational playbooks — Apache 2.0, no vendor lock-in.
319 Microservices
~20 AI Agents
40+ Playbooks
72h+ Offline
The Problem
Why EV charging is broken
Chargers are expensive vending machines — zero intelligence at the edge. The industry loses an estimated $25–30B annually to failures that onboard AI could prevent.
Lose connectivity, lose everything. No fallback, no local decision-making — contributing to 15–25% charger downtime rates across public networks.
Each CPO builds its own stack. No interoperability, no shared intelligence — every operator reinvents the same broken wheel with no common protocol baseline.
Maintenance is break-fix with industry-average MTTR exceeding 96 hours. Faults are discovered by angry drivers, not predictive AI.
How It Works
Three-layer intelligence distribution
AuralinkLM-675B (MoE) handles fleet analytics, model training, and billing aggregation. Only 5% of compute — focused on long-term pattern learning across the entire fleet.
AuralinkLM-14B (INT4 GGUF) drives site-level orchestration, predictive maintenance, and local AI inference — delivering 13–15× lower latency than cloud inference. 30% of compute.
AuralinkLM-0.5B (INT4) runs per-charger fault detection, load balancing, and session management with no network dependency. 65% of compute — 72h+ autonomous operation.
Performance
Research-validated performance on an 18,000-incident test corpus
87.6%
Diagnostic accuracy (F1=0.862)
28–48ms
Edge inference latency (P50 TTFT)
72h+
Autonomous offline operation
78%
Autonomous incident resolution
47pt
Accuracy gain over base models
4–8h
Mean time to repair (vs 96h baseline)
Deep Dive
Deep-dive into the Auralink architecture. NDA required for access.
These documents contain proprietary architecture details, benchmarks, and implementation specifics. A quick NDA protects both parties.
Market analysis, value proposition, and competitive positioning of the Auralink SDC platform.
Academic-grade analysis of edge AI for EV charging: architectures, benchmarks, and novel contributions.
Complete system architecture: 319 microservices, AI agent framework, deployment topology, and integration protocols.
Source Code
View the GitHub repositories and HuggingFace models. Requires NDA acceptance first.
Please sign the NDA first before requesting code access.
Partnership
Looking for CPO partners to deploy Auralink on live networks. OCPP 2.0.1 compliant, Apache 2.0 licensed — no lock-in, no proprietary hardware dependency.
Deploy Auralink on your charging network. Full OCPP 2.0.1 support, plug-and-play with ABB Terra AC chargers and most OCPI 2.2.1 compatible hardware.
Embed AuralinkLM models and the agent framework into your charging products. API-first design, Docker-native, with ISO 15118 integration support.
Test, contribute, and help shape the future of intelligent charging. Apache 2.0 licensed — the full 319-microservice platform, AI models, and agent framework.
Timeline
Auralink will be open-sourced progressively after integration testing with real hardware.
Core platform complete: 319 microservices, AuralinkLM model family (675B/14B/0.5B), HMAO agent framework, OCPP 2.0.1 protocol stack (7,085 LOC), trained on 176,041 domain examples.
Real-world deployment with CPO partners. Validation on ABB Terra AC chargers, live OCPP 2.0.1 network testing, and edge hardware benchmarking on AMD Ryzen AI Max+ platforms.
H1 2026 milestones: OCPP 2.1 certification, ISO 15118 Plug & Charge, V2G orchestration, enhanced fleet analytics. Limited open-source release for community testing.
Complete platform release under Apache 2.0. All microservices, AuralinkLM models on HuggingFace, agent framework, and protocol stacks — community-driven development.
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