Reliability
AI systems engineered to the same uptime and recovery standards as critical infrastructure.
AI Solutions
Enterprise AI, engineered as production infrastructure.
We design, deploy and operate AI systems as load-bearing components of the enterprise — from architecture and model integration to GPU-ready infrastructure and MLOps governance.
Strategic positioning
AI is no longer an experiment. It is critical infrastructure.
Proxy Energy builds enterprise AI systems with the same engineering rigor we apply to networks, datacenters and security perimeters. We treat models, retrieval layers and inference platforms as production assets — observable, governed and accountable.
Our teams operate across the full AI value chain: strategy, data foundations, model selection and integration, GPU and inference infrastructure, and the MLOps discipline required to keep AI systems reliable, compliant and aligned with business outcomes.
Business challenges
The operational realities of enterprise AI.
We engineer past the prototypes that never reach production.
Challenge
AI proofs-of-concept that never reach production
Outcome
A production-ready AI architecture with deployment, observability and governance from day one.
Challenge
Fragmented data and unreliable model inputs
Outcome
Governed pipelines feeding AI systems with high-quality, observable, lineage-tracked data.
Challenge
Unpredictable GPU costs and inference latency
Outcome
Right-sized inference clusters, batching strategies and FinOps discipline across model serving.
Challenge
Regulatory exposure on AI workloads
Outcome
GDPR-aware deployments, audit trails and policy controls aligned with European jurisdiction.
Challenge
Models silently degrading in production
Outcome
Continuous monitoring, drift detection and evaluation harnesses across the model lifecycle.
Challenge
Vendor lock-in on opaque AI platforms
Outcome
Portable architectures that combine open and proprietary models without strategic dependency.
Core capabilities
A complete AI engineering practice.
Strategy, systems and operations under one accountable engineering team.
Enterprise AI strategy
Roadmaps, opportunity mapping and architecture blueprints for AI at organizational scale.
- Use-case discovery
- ROI framing
- Reference architectures
- Build vs buy
Machine learning systems
End-to-end ML platforms covering training, evaluation, serving and continuous improvement.
- Training pipelines
- Feature stores
- Evaluation harnesses
- Experimentation
AI model integration
Integration of foundation and specialized models into enterprise workflows and applications.
- LLM orchestration
- Retrieval (RAG)
- Agentic systems
- Tool calling
AI APIs & platforms
Internal AI APIs, gateways and platform services that make models consumable across teams.
- AI gateway
- Rate limiting
- Tenant isolation
- SDKs
Data pipelines for AI
Streaming and batch pipelines that feed AI systems with governed, observable data assets.
- Ingestion
- Quality
- Lineage
- Governance
GPU & HPC-ready infrastructure
Compute fabrics engineered for training, fine-tuning and high-throughput inference.
- GPU clusters
- Inference fleets
- Scheduling
- Cost engineering
MLOps & governance
Deployment, observability, drift detection and audit trails across the model lifecycle.
- CI/CD for ML
- Model registry
- Monitoring
- Approval workflows
GDPR-aware AI deployment
Privacy-by-design AI architectures aligned with European regulatory requirements.
- Data residency
- PII handling
- Access controls
- Audit logging
Technical approach
Models as services. Services as infrastructure.
We treat AI workloads as first-class infrastructure: containerized, observable, version-controlled and deployed through the same engineering discipline as any other production system. Inference is engineered for latency budgets; training is engineered for cost discipline; integrations are engineered for resilience.
Our reference architectures combine open and proprietary models behind a unified AI gateway — providing tenant isolation, observability and governance without locking the enterprise into a single vendor.
Reference stack
- LLM gateways & orchestration
- Retrieval-augmented architectures
- Vector and hybrid search
- Model serving (CPU & GPU)
- Kubernetes & container orchestration
- Streaming & batch data pipelines
- Observability for AI workloads
- Policy and access controls
Enterprise benefits
Why this approach changes the trajectory.
Sovereignty
Deployments aligned with European jurisdiction and data-residency requirements.
Governance
Audit trails, policy controls and lifecycle management for regulated environments.
Cost discipline
Inference and training infrastructure engineered for predictable, observable economics.
Portability
Architectures designed to combine open and proprietary models without lock-in.
Velocity
Platform services and reusable patterns that compound across business teams.
Implementation methodology
A disciplined path from strategy to operations.
01
Strategic discovery
Operational, technical and regulatory assessment of the target environment.
02
Architecture design
End-to-end blueprint covering compute, data, security and operational layers.
03
Implementation
Iterative build with code review, infrastructure-as-code and continuous integration.
04
Deployment & hardening
Controlled rollout with hardening, observability and rollback playbooks.
05
Monitoring & optimization
SLOs, performance, cost and reliability engineered as continuous loops.
06
Long-term partnership
Evolution roadmap, senior on-call expertise and 24/7 operational coverage.
Security & compliance
AI deployed under European engineering standards.
Security-by-design and compliance-aware operations across every AI workload we engineer.
- Data residency and sovereign deployment options across EU jurisdictions
- PII handling, redaction and policy controls integrated at the gateway layer
- Audit logging across model invocations, data access and infrastructure changes
- Role-based access controls and tenant isolation across multi-team platforms
- Encryption in transit and at rest across data, model and inference layers
- GDPR-aware data lifecycle, retention and right-to-erasure workflows
Related solutions
Explore the rest of the ecosystem.
Engineered to work as one architecture across AI, software, infrastructure, hosting, security and data.
Engineer your AI roadmap
Talk to a senior AI engineer about your roadmap.
Architecture, GPU infrastructure, MLOps, model integration or AI governance — one accountable team for the full stack.