We build the systems between raw data and production AI.
AIntelligence Systems designs and operates MLOps pipelines, retrieval-augmented generation systems, and cloud AI infrastructure — then trains your team to run it.
Four disciplines, one delivery team.
We don't hand off between teams. The engineers who design your pipeline are the ones who operate it in production.
MLOps & ML Platform Engineering
End-to-end pipelines that take models from notebook to production, with versioning, CI/CD, and rollback built in from day one.
- Kubernetes-native training & serving pipelines
- CI/CD for models, not just code
- Cost-aware autoscaling on GPU nodes
RAG & Applied LLM Systems
Retrieval-augmented systems that ground language models in your organization's own data, with evaluation baked into every release.
- Vector index design & retrieval tuning
- Multi-agent and tool-using architectures
- Hallucination and grounding evaluation
AI & Cloud Infrastructure
Infrastructure-as-code foundations across AWS, GCP, and Azure, purpose-built for the load patterns of ML and inference workloads.
- Terraform-first, multi-cloud architecture
- IRSA, networking, and secrets management
- Production-grade observability stacks
Cloud & AI Training
Cohort and enterprise programs that build real Kubernetes and applied ML capability inside your team, not just certificates.
- Hands-on Kubernetes & cloud infra tracks
- Applied MLOps and LLM engineering workshops
- Cohort mentorship for certification prep
The same pipeline, every engagement.
Whether we're standing up your first inference endpoint or hardening a system already in production, work moves through four stages.
Ingest & Version
We map your data sources, establish versioning and lineage, and define the ground truth your models and retrieval systems will be evaluated against.
Train & Evaluate
Training and fine-tuning runs are reproducible and benchmarked, with evaluation harnesses that catch regressions before they reach a user.
Deploy & Retrieve
Models and retrieval systems ship behind infrastructure built to scale — autoscaled inference, tuned vector search, and clean rollback paths.
Monitor & Retrain
Once live, we instrument for drift, latency, and cost, and put a retraining loop in place so the system keeps improving after we hand it over.
Capability your team keeps after we leave.
Structured tracks for engineers moving into cloud, Kubernetes, and applied AI — built from the same systems we run in production.
Programs run as cohort workshops or embedded team training, and are designed for engineers who need working systems knowledge, not just slides.
Let's build the infrastructure your AI actually needs.
Tell us what you're running today — we'll tell you what it takes to get it into production and keep it there.