AI Infrastructure
The Foundation Everything Else Runs On.
You can’t build reliable AI systems on a shaky foundation. Data that’s scattered across three systems. No API connections between your core platforms. Inconsistent data quality that makes AI outputs unreliable. These aren’t edge cases, they’re the reality for most growing businesses.
Getting AI infrastructure right isn’t glamorous, but it’s the work that determines whether your AI investment compounds over time or costs you money to maintain.
What Infrastructure Work Covers
- Data consolidation and pipeline design
- API connections between core business systems
- LLM integration architecture, model selection, prompt management, cost control
- Vector database setup for knowledge retrieval systems
- Monitoring, logging, and performance tracking for AI systems
- Cloud architecture for scalable AI workloads
Model Selection and Cost Management
Businesses often spend 3-5x more than necessary on AI compute because they’re running the wrong model for the job. GPT-4 isn’t the right choice for every task. We match model capability to use case, implement caching strategies, and set up monitoring so you always know what your AI systems are actually costing.
Our LLM Cost Calculator can give you a quick estimate before we even talk. Try it on our Tools page.
Who This Is For
Companies preparing to scale AI adoption. Businesses that tried to deploy AI and ran into data quality or integration issues. Technical teams that need a senior AI architecture review before building. We’ve seen the mistakes. We can help you avoid them.
Talk to us before you start building. An infrastructure conversation early saves significant money later.