AI-native infrastructure engineering
Where AI meets
modern infrastructure
EdgePath Networks builds secure, observable, high-performance systems at the intersection of artificial intelligence and production infrastructure.
What we do
Engineering at the AI–infrastructure boundary
EdgePath works where AI systems meet real infrastructure — networking, observability, security, and platform engineering. We focus on the layer most teams skip over: the production systems that AI-enabled products depend on to actually run.
Our core capability sits in Linux datapaths, eBPF, Kubernetes, and backend systems engineering. We build the serving infrastructure, data pipelines, and networking that make AI workloads reliable and performant — not just functional in a notebook.
This is not surface-level AI integration. We work with teams that need real depth: custom kernel-level telemetry, high-throughput inference infrastructure, secure service boundaries for AI agents, and the operational foundations that keep it all running in production.
Why us
Built for technically demanding work
Production-grade systems thinking
We design for reliability, performance, and operational clarity. Every system we build is meant to run in production, not just pass a demo.
Deep networking and Linux expertise
From kernel datapaths to L7 proxies, we bring genuine depth in networking, eBPF, and systems programming — not just cloud API wrappers.
Practical AI integration
We help teams build the infrastructure AI products actually need: serving pipelines, data platforms, intelligent networking, and tooling that works.
Services
What we deliver
AI Infrastructure Engineering
Build reliable foundations for AI-enabled products and internal platforms.
- Model serving infrastructure and inference optimization
- GPU orchestration and resource scheduling
- AI pipeline reliability and fault tolerance
- LLM integration and prompt-routing architectures
- Vector database deployment and tuning
- Cost modeling and capacity planning for ML workloads
Network Observability and Traffic Intelligence
Visibility into real traffic, protocol behavior, and production systems.
- Deep packet inspection and protocol decoding
- Flow analysis and traffic baselining
- Anomaly detection and automated alerting
- Network performance monitoring and bottleneck identification
- Protocol-aware observability pipelines
- Integration with existing monitoring stacks
eBPF and Linux Datapath Engineering
High-performance telemetry, control, and packet-level systems.
- Custom eBPF program development and lifecycle management
- XDP-based packet processing and filtering
- TC hook programming for traffic shaping and classification
- Kernel-level networking and socket-layer instrumentation
- Performance tracing and profiling with BPF tooling
- Security enforcement at the datapath level
Secure Platform and Proxy Systems
Policy enforcement, secure service boundaries, and AI-agent-friendly control layers.
- L7 proxy deployment and custom filter development
- mTLS rollout and certificate lifecycle automation
- Zero-trust networking architecture and implementation
- Service mesh configuration and operations
- API gateway design and rate limiting
- Identity-aware routing and access policy enforcement
Cloud-Native Backend and Systems Development
Go-based services, Kubernetes integrations, APIs, automation, and operations.
- Microservice development in Go
- Kubernetes operator and controller implementation
- CI/CD pipeline design and automation
- Infrastructure-as-code and provisioning automation
- API design, versioning, and contract testing
- Operational tooling and developer experience improvements
Technologies
Tools of the trade
We work across the modern infrastructure stack.
languages
systems
platforms
networking
observability
data
Let's build something.
Whether you're scaling AI infrastructure, building network observability, or need deep systems engineering — we'd like to hear about it.
Get in touchOr email us at contact@edgepath.io