Our services
We don't build models.
We build what's underneath.
Consulting, implementation, and ongoing maintenance for AI infrastructure. Three engagement types, one goal: making your AI systems reliable, cost-efficient, and scalable.
01 — Consulting
You pay for our expertise,
not our labor.
We audit your AI workload, diagnose infrastructure problems, and give you a clear prioritized picture of what's broken and what to do about it. If you don't have an AI workload yet and want to move off platforms and own your infrastructure, we architect that path for you.
$3–5K
Per engagement
1–2 wk
Timeline
Deliverable
Always a document — a report, a roadmap, or an architecture specification. No code is written in this engagement.
Domain coverage
Engagement types
- —Audit an existing AI workload and surface opportunities
- —Diagnose a known problem and prescribe a solution
- —Design architecture for moving from managed platforms to owned infrastructure
02 — Implementation
You pay for the work to get done.
We build, fix, migrate, or optimize your AI infrastructure. Sometimes clients come here after Consulting. Sometimes they already know what needs to be built and skip straight here.
$8–15K
Per project
4–8 wk
Per sprint
Model Serving & Deployment
Deploying models on Kubernetes with GPU scheduling using vLLM, TGI, or Triton Inference Server. Autoscaling inference endpoints, multi-model serving, A/B testing infrastructure, and canary deployments.
RAG Infrastructure
Vector database setup and optimization (Qdrant, Weaviate, Pinecone). Chunking strategies, retrieval pipeline architecture, embedding model deployment, hybrid search, caching layers, and evaluation frameworks.
GPU Cost Optimization
Right-sizing GPU instances, spot and preemptible instance strategies, inference batching, model quantization (GPTQ, AWQ, GGUF), multi-tenancy for GPU sharing, and reserved capacity planning.
AI Platform Engineering
Building the internal developer platform that lets ML/AI teams ship without touching infrastructure. CI/CD for models, experiment tracking, model registry, feature stores, and self-service deployment workflows.
Cloud Architecture for AI
Designing the underlying AWS/GCP/Azure infrastructure that supports all of the above. Networking, storage, security, IAM for AI workloads, multi-region strategies, and infrastructure-as-code (Terraform/Pulumi).
Deliverable
Working infrastructure, IaC code, architecture documentation, runbooks, and a handoff session so your team can maintain what we built.
03 — Maintenance
We keep it running,
optimized, and evolving.
Ongoing embedded support post-implementation. We monitor, optimize, and evolve your infrastructure as your product and traffic grow.
$3–6K
Per month
3 mo
Minimum
Scope
- —Monthly infrastructure health reviews
- —Inference cost monitoring and optimization reports
- —GPU capacity planning as usage scales
- —Model serving updates (new model versions, framework upgrades)
- —Architecture advisory for new features and products
- —Incident support and troubleshooting (SLA-based)
- —Quarterly infrastructure roadmap planning
Deliverable
Monthly Ops Report with cost trends, performance metrics, and recommended actions. Quarterly roadmap review. Ongoing async access for technical questions and incident support.
How it works
Enter at any stage
Suspect a problem?
A CTO who suspects they're overpaying for GPU compute but isn't sure? Start with Consulting.
Know what to build?
A startup that already has a roadmap and needs someone to execute? Skip straight to Implementation.
Need ongoing support?
A team that just finished an implementation and needs someone to keep it running? Maintenance Contract.
Have a project in mind?
Tell us about your AI infrastructure challenges and we'll scope an engagement that fits.
Start a conversation