AI prototypes are easy. Production systems are engineering.
Your AI works in the demo. We make it work at scale — designing, building and operationalizing the systems and cloud architecture underneath.
Three areas, one team that has shipped all of them in production.
Agentic AI systems
Agent runtimes, orchestration and evaluation for LLM-driven workflows that have to be correct, observable and cost-controlled.
- Agent architecture and frameworks
- MCP servers and tool gateways
- Memory, retrieval and knowledge graphs
From concept to production
The full engineering lifecycle — from an early hypothesis to a production system operated and improved at scale.
- Concept, architecture & PoC / PoV
- Development, testing & deployment
- Scale, optimization & improvement
Cloud-native platforms
Multi-tenant SaaS backends and Kubernetes platforms engineered for reliability and clean operations.
- Python and Go microservices
- Kubernetes on AWS, GitOps, observability
- Graph, vector and relational data stores
Your engineering team, extended.
Think of InferAxis as your engineering team for hire — or an extension of the team you already have.
We step into complex technical problems, shape the architecture and turn it into working software, quickly. Your team stays focused on the product while we own the engineering complexity underneath.
We can own the whole engineering journey.
Concept
The opportunity, workflow and measurable outcome.
Prove
A focused PoC / PoV to validate approach and value.
Engineer
Build, test, secure, deploy and instrument.
Optimize
Leaner, faster, more reliable and cost-efficient.
Have a complex system you need engineered?
Tell us what you are building. Let's talk about the engineering.