AI that stays
behind your firewall
PacketNAP builds and runs AI inside your perimeter. Enterprise Claude, ChatGPT, or Microsoft Copilot where cloud is fine. A local Llama or Qwen model on your own hardware when your data can’t leave the building.
Where AI gets stuck in real IT
If any of this sounds familiar, we’ve probably already built the fix for someone in your vertical.
Five ways we put AI to work
Pick one. Pick a few. Start where the pain is loudest.
- Deploy Llama, Qwen, Mistral, or DeepSeek in your colo, your own datacenter, or the PacketNAP private cloud. Nothing touches OpenAI, Anthropic, Google, or AWS.
- Full chat, RAG, and agent capability with zero outbound egress.
- Meets HIPAA, SOC 2, PCI, legal privilege, and export-control requirements.
- Right-sized hardware. You do not need an H100 cluster to run useful AI.
- Password resets, MFA issues, VPN access, SaaS provisioning, server status checks.
- Deflection rates of 60 to 80% on repetitive tickets.
- Integrates with ServiceNow, Jira / Atlassian, Freshservice, Zendesk, or a custom system.
- Your choice of backend. Cloud (Azure OpenAI) or fully private (local models).
- Voice, chat, and email share the same brain.
- Live-agent handoff carries full conversation context.
- Guardrails catch hallucinated policies before they reach a customer. No leaked PII.
- Runs on your stack or ours. On-prem option for regulated verticals.
- Natural-language queries against your warehouse.
- Automated reporting, anomaly detection, churn and revenue signals.
- Your numbers never leave your boundary. Local LLM by default.
- Works with Microsoft Fabric, Snowflake, Databricks, Postgres, SQL Server.
- M365 Copilot licensing, governance, adoption tracking, and ROI measurement.
- Custom agents for contract review, invoicing, or ops reporting.
- Data guardrails, SSO, audit logs, compliance alignment.
- Official “Claude and ChatGPT at work,” sanctioned and logged.
Field report: Chicago
50 support staff, 25,000+ customer accounts, and no cloud AI allowed.
Their customer MSAs blocked every cloud AI vendor they tried. Renegotiating 25,000 contracts was not on the table. So they built on their own metal, with us.
We deployed a private LLM stack in their PacketNAP Chicago colo. It checks ticket status, looks up billing, probes server reachability, walks customers through fixes, and escalates with full context when it needs to. No customer data leaves the perimeter.
“Every AI vendor we looked at needed our customer data to leave the building. Our MSAs forbid that. PacketNAP built us a private AI stack in our own colo, in thirteen weeks, for $250K, with zero customer contracts touched. Two-thirds of our tier-1 tickets never reach a human anymore, and our CSAT went up. I can’t ask for more than that.” VP of Operations · Chicago · name withheld per MSA
How we work
Four stages. Fixed scope. Measured outcomes.
Assess
Discovery call, use-case scoring, readiness report, written recommendation. You leave knowing what to build, what it costs, and whether you need us to do the build.
Pilot
One focused use case, one measurable goal. We ship a working system, not a slide deck. Scale it or stop based on the result.
Roll Out
Production deploy, security review, user training, integration with your existing stack. The adoption work that turns “we have AI” into “we use AI.”
Operate
Managed AI services. Monitoring, cost control, model drift, compliance checks, quarterly roadmap reviews. Your AI stays useful instead of rotting.
Why PacketNAP
We already run your
infrastructure
For PacketNAP colocation and private cloud customers, AI is the next layer on the same platform. The engineers who manage your network and servers are the ones building and running your AI. One vendor. One SLA. One invoice.
No vendor
lock-in agenda
This isn’t an Azure shop, and it isn’t an AWS shop. We pick the model that fits your data, your compliance posture, and your budget. Sometimes that means a local 70B model. Sometimes it means Copilot. We don’t care which one wins.
Security first,
measured outcomes
Every engagement starts with a data-boundary review. No pilot touches production data without explicit scoping. Prompts and responses are logged. Business metrics get set before kickoff. If it isn’t working by week 8, we say so.
Frequently
asked
questions
How much does this cost, and how long does it take?
The Readiness Assessment is free. A focused Private AI Starter (one use case, 2 to 4 weeks to production) typically lands in the low six figures. Full private-AI builds like the Chicago service-provider project ran $250,000 all-in, including hardware, and took 13 weeks. Cloud-only engagements (Copilot rollout, Azure OpenAI integration) come in lower since there’s no hardware involved.
Every engagement is price-scoped during the free assessment, so you never see a surprise invoice.
How do you protect our data?
Every engagement starts with a data-boundary review. For regulated workloads the default is private on-premise LLMs. Your data stays in your building or our colo. Every prompt and response is logged and auditable. SSO and RBAC come standard.
Where cloud AI is appropriate we use enterprise endpoints with no data retention (Azure OpenAI, Anthropic Enterprise, OpenAI Enterprise). Never the consumer APIs that train on your inputs.
How do you handle hallucinations, governance, and compliance risk?
Four layers.
Retrieval-grounded generation: the model cites its sources, and uncited responses are blocked on high-risk queries. Schema-checked outputs: structured responses are validated; free-text is filtered for policy violations. Continuous evaluation: a test suite of real-world queries runs against every model update, and regressions block deployment. Signed audit logs: every query and response is stored, hash-signed, and searchable. You can tell an auditor exactly what the AI said and who asked.
Which AI platforms and vendors do you work with?
Mainstream cloud: Microsoft Azure AI and Copilot, OpenAI (ChatGPT Enterprise, GPT-4/5-class APIs), Anthropic Claude, AWS Bedrock, Google Cloud Vertex AI.
On-premise and private: Meta Llama 3.x, Qwen, Mistral, DeepSeek. We run them on vLLM, Ollama, or llama.cpp depending on fit. There’s no horse in the race on our side. We pick whatever best matches your data, your latency budget, and your compliance posture.
How do you measure ROI?
One to three business metrics get picked with you during the Assessment. Concrete things like ticket deflection rate, hours saved per week, cost per resolved ticket, or time-to-insight on a reporting query. Baselines are captured before any code ships. Every milestone after that is measured against the baseline. No vanity metrics, no “AI score.”
Ready to put AI to work?
Start with a free 30-minute Readiness Assessment. By the end you’ll know which use case is worth piloting, what it will cost, and whether you need us to do the build.