AI Infrastructure
AI Infrastructure for Business Automation
This engagement turned scattered AI experiments into dependable infrastructure. The emphasis was on privacy, repeatability, and real integration with the systems the business already used.
Problem
A business wanted to move AI from ad-hoc experiments to dependable infrastructure that could run private workflows, keep data in-house, and integrate with existing tools.
Constraints
- Sensitive data had to remain in a private environment
- Workflows needed to be repeatable, not one-off
- Infrastructure had to fit a realistic hardware budget
Technical Approach
- Sized GPU and compute capacity to real workloads
- Deployed a private LLM serving stack
- Added embeddings and a vector database for retrieval
- Built automation pipelines with clear inputs and outputs
- Integrated workflows with existing business systems
Architecture Decisions
- Kept inference private to protect sensitive data
- Separated model serving from automation logic
- Standardized retrieval patterns for reuse
- Containerized components for portability
Outcome
- Repeatable, private AI workflows
- Reduced manual operational effort
- Clear integration points with business tools
Lessons Learned
- Practical AI value comes from workflows, not models alone
- Privacy and integration should shape the architecture early
- Right-sized hardware beats speculative over-provisioning
Ready to bring clarity to your infrastructure?
If your systems are becoming expensive, complex, unreliable, or difficult to scale, let's review the architecture and build a better path forward.