⚡
AI Infrastructure
Cloud architecture designed from the ground up for AI workloads.
🔒
99.9% uptime
SLA for AI endpoints
💰
40% lower cost
vs naive cloud setups
⚡
< 200ms P99
latency on vector search
⚡AI INFRA
P99 < 200ms
🗄️
Vector DB📊
Metrics🔄
CI/CD☁️
AWS / GCP🐳
Containers📡
API LayerAbout
We design and implement the infrastructure layer that makes AI agents fast, reliable, and cost-efficient at scale. Vector databases, GPU instances, model serving, MCP servers, and CI/CD pipelines purpose-built for AI systems.
Our Process
1
Architecture Review3–5 days
Audit current infra and design AI-ready target state
2
Vector DB Setup2–3 days
Select and configure the right vector store for your use case
3
Model Serving1 week
Deploy model endpoints with caching, rate limiting, and fallbacks
4
CI/CD Pipeline3–5 days
Automated testing and deployment for AI components
5
Observability2–3 days
Logging, tracing, cost monitoring for every AI call
Tech Stack
Infrastructure
AWS / GCPRailwayDocker / ECSGitHub Actions
Data
Pineconepgvector
Orchestration
MCP Protocol
Ideal For
- ✓Companies scaling AI beyond prototypes
- ✓Engineering teams adding AI to existing platforms
- ✓Startups that can't afford downtime on AI features
Ready to Get Started?
Let's discuss your project and find the best solution for your needs.
Get Started →