AI Infrastructure
AI moves fast. Your infrastructure needs to move faster. We build the foundation for self-hosted models, MCP servers, agent orchestration, and GPU clusters — so you can ship AI features without infrastructure bottlenecks.
What We Deliver
MCP Server Development
Build Model Context Protocol servers that connect Claude and other LLMs to your internal systems. Custom tools, data sources, and integrations — all production-ready.
- Custom MCP server development
- Database & API tool integrations
- Authentication & authorization
- Deployment on your infrastructure
- Claude Desktop & API integration
LLM Deployment & Serving
Self-host open-source models like Llama, Mistral, or your fine-tuned models. High-throughput inference with vLLM, TGI, or custom serving solutions.
- vLLM / TGI deployment
- Model quantization (GPTQ, AWQ)
- Auto-scaling inference
- A/B testing & model routing
- Cost optimization strategies
GPU Cluster Infrastructure
Set up GPU infrastructure on AWS, GCP, or dedicated providers. From single A100s to multi-node clusters. Optimized for training or inference workloads.
- AWS EC2 P4/P5 / GCP A3 setup
- Kubernetes GPU scheduling
- Multi-GPU training infrastructure
- Spot/preemptible GPU strategies
- Cost monitoring & optimization
AI Agent Orchestration
Infrastructure for multi-agent systems. LangChain, CrewAI, AutoGen — we set up the orchestration layer, tool execution, and state management.
- Agent framework deployment
- Tool execution sandboxing
- State & memory management
- Observability & tracing
- Rate limiting & cost controls
RAG Pipeline Infrastructure
Production-grade RAG pipelines with vector databases, embedding models, and retrieval optimization. Built for scale and accuracy.
- Vector DB setup (Pinecone, Weaviate, Qdrant)
- Embedding pipeline architecture
- Chunking & indexing strategies
- Hybrid search implementation
- Reranking & relevance tuning
MLOps & Model Lifecycle
End-to-end ML infrastructure. Experiment tracking, model registry, feature stores, and deployment pipelines. Reproducible, auditable, production-grade.
- MLflow / Weights & Biases setup
- Model registry & versioning
- Feature store implementation
- Training pipeline automation
- Model monitoring & drift detection
Our AI Tech Stack
vLLM, TGI, Triton, Ollama
LangChain, CrewAI, AutoGen, MCP
Pinecone, Weaviate, Qdrant, pgvector
NVIDIA A100/H100, AWS P4/P5, GCP A3
MLflow, W&B, Kubeflow, Ray
Llama, Mistral, Claude, GPT-4
Use Cases We've Built
Internal Knowledge Assistant
RAG pipeline over company docs with MCP integration for Claude. Employees query internal knowledge via Slack or web interface.
Customer Support Automation
AI agents that handle L1 support, escalate complex issues, and learn from resolutions. Integrated with existing ticketing systems.
Code Review Agent
MCP server that gives Claude access to your codebase, CI/CD, and docs. Automated code review with context-aware suggestions.
Self-Hosted LLM Platform
Private LLM deployment for data-sensitive workloads. Llama/Mistral on your infrastructure with API-compatible interface.
Ready to Build Your AI Infrastructure?
Get a free technical briefing. We'll assess your AI use case and design the infrastructure to support it at scale.