Seven ways we help you run AI on your terms.
Pick a single service or combine them. Every engagement is scoped in phases with a clear deliverable, so you can stop, pause or continue at each step. Most clients start with an assessment, then move into a pilot and production build.
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Learn more →GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Learn more →Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Learn more →MLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring so models ship like software.
Learn more →Custom Model Fine-Tuning
Adapt open models to your language, documents and workflows with your own data.
Learn more →AI Security & Compliance Audit
Assess model risk, data privacy and controls before regulators or customers ask.
Learn more →Ongoing Managed AI Support
A monthly retainer for monitoring, upgrades and on-call support of your AI stack.
Learn more →START HERETwo-week AI Infrastructure Assessment
The fastest way to find out which services you need. Fixed scope, fixed fee, and a written report with architecture, cost range and a 90-day plan.
Book it →Where each service starts, and what it produces.
| Service | First phase | Headline deliverable | Typical signal |
|---|---|---|---|
| Private & On-Prem LLM Deployment | Workloads, data sensitivity, hardware inventory, latency targets | Private inference endpoint | 4–8 wks |
| GPU Infrastructure Optimization | Telemetry from DCGM, Prometheus and cloud billing | Savings plan | 2 wks |
| Cloud-to-AI Migration | Inventory systems, data gravity, compliance scope | Wave plan | 100% |
| MLOps Pipeline Setup | Current workflow, tools, pain points | End-to-end pipeline | Every run |
| Custom Model Fine-Tuning | Target task, success metric, data audit | Tuned model weights | Baseline |
| AI Security & Compliance Audit | Systems, vendors, data flows | Risk register | Hands-on |
| Ongoing Managed AI Support | Access, runbooks, alert routing | Monthly ops report | < 1 hr |
Common paths clients take
Private AI from scratch
Assessment → Private LLM Deployment → MLOps Pipeline Setup → Managed Support.
Cut the AI bill
Assessment → GPU Optimization → Custom Fine-Tuning for smaller models → Managed Support.
Prove it's safe
AI Security & Compliance Audit → remediation → Private LLM Deployment for high-risk use cases.
Get the data ready
Cloud-to-AI Migration → MLOps Pipeline Setup → first production use case.
Find out what private AI would cost you.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.