Capability 06 — Managed Services
Your AI, operated 24/7.
AI in production is not a project — it's an operation. Models need updates. GPUs need monitoring. Security needs patching. Costs need optimization. We run it all so your team doesn't have to.
Scope
What's included.
24/7 monitoring
GPU health, model latency, error rates, throughput. We see problems before your users do.
Model updates
When a new open-weight model is released — and they're released weekly — we evaluate it against your workload and upgrade when it's better.
Security patches
CVEs, firmware updates, dependency vulnerabilities. We track, test, and deploy.
Cost optimization
GPU utilization tracking, inference cost per token, batch scheduling. We make your infrastructure efficient.
Support
Your team has a direct line to engineers who know your stack. Not a ticket queue. Not a chatbot.
Coverage
Departments we serve.
AI doesn't live in IT. Every department can use it. We manage AI across the organization.
| Department | What we operate | Example |
|---|---|---|
| Finance | RAG pipelines, reporting agents | Automated P&L analysis from ERP data |
| Marketing | Content generation, analytics agents | Campaign performance summaries with source data |
| Sales | CRM-connected RAG, proposal agents | "What did we pitch Acme Corp in Q3 2025?" |
| Customer Service | Support agents, knowledge base RAG | Tier-1 automation with human escalation |
| Operations | Monitoring agents, SOP RAG | Infrastructure alert → diagnosis → fix |
| Administrative | Document processing, policy RAG | Contract review, compliance checks |
| IT | Diagnostics agents, runbook automation | Incident response, patch management |
Commercial
Pricing model.
Monthly recurring. Predictable cost. No surprises.
Hardware procurement or hosted GPU allocation.
Monitoring, updates, security, support.
Ongoing tuning, model upgrades, cost management.
One invoice. One relationship. One team that knows your entire stack.
Rationale
Why managed services?
You don't need an internal ML team.Staffing 24/7 AI operations in-house means at least two ML engineers, a security engineer, and a DevOps person — well over half a million dollars a year before you've bought a single GPU.
Or you work with us.One monthly payment. One team that's done this before. One number to call when something breaks at 3am. A fraction of the cost of building that team in-house.
| Before MSP | After MSP | |
|---|---|---|
| Internal AI team | 3 engineers (partial) | 0 |
| Model maintenance | 30% of engineering time | 0 |
| Security patches | 2 weeks behind on average | Applied within 24 hours |
| Model upgrades | Ad-hoc, when someone remembers | Evaluated weekly, deployed when better |
| 3am incident | On-call engineer woken up | We handle it |
| Monthly cost | Loaded salaries + hardware | One predictable invoice |
Illustrative scenario — a regional bank running AI for loan document processing
Contact
Let's talk about what it costs to run your AI.
We'll give you a clear, line-by-line estimate. No bait pricing. No hidden fees.
Talk to us about MSP →