Key facts
| Target environments | Isolated management, OSS/BSS and lab network segments |
| Data boundary | Subscriber and network data never traverse an external path |
| Models | Open-weight models suitable for offline operation from the catalogue |
| API contract | OpenAI-compatible endpoints; identical application code across tiers |
| Updates | Controlled intake with checksums, staging and rollback |
| Access | SSO/SCIM for staff and scoped service keys inside the network |
| Audit | Admin, key and inference events export to internal SIEM |
| Status | Live platform; enterprise deployment terms per engagement |
TL;DR
- Deploy per network segment: NOC, OSS/BSS or lab, each with its own boundary.
- Keep subscriber data inside the perimeter and minimise identifiers anyway.
- Treat model updates as controlled releases aligned to change windows.
- Route work by segment; not every workload needs the largest local model.
- Stream audit events into the SIEM your operations teams already use.
How it works, step by step
- Map candidate workloads to network segments: alarm summarisation in the NOC, ticket enrichment in OSS/BSS, documentation search in engineering.
- Confirm which segments forbid egress, and size GPU capacity per segment rather than for a single shared estate.
- Deploy the runtime and verified open-weight models, with a version manifest per segment.
- Integrate SSO/SCIM for operations staff and scoped keys for services; keep change execution in existing change management.
- Design read-only tools for telemetry, inventory and ticketing, and route every proposed write to a human queue.
- Schedule updates as controlled bundles aligned to maintenance windows, with staging validation and rollback.
- Stream inference, key and admin events to internal SIEM, then pilot one queue and compare outcomes against manual handling.
Try it yourself
Open the private LLM deployment estimator →
Segmenting the estate
Telco networks already separate management, operational and lab environments, and air-gapped AI should follow those boundaries rather than invent new ones. Each segment gets its own runtime, model set and audit path, sized for the workloads it hosts. A NOC segment may need fast small models for alarm summarisation; an engineering segment may prefer a larger model for document reasoning.
Because Plugsky exposes identical OpenAI-compatible endpoints in every tier, applications move between segments and connected estates without change. That keeps your tooling, prompt libraries and evaluation suites reusable across a naturally fragmented environment.
Operations, change control and updates
Agents should observe and explain, not reconfigure. Keep tools read-only against telemetry, inventory and ticketing, and let proposed changes flow through the same change management that governs the network today. That preserves existing accountability and makes adoption easier for operations teams.
- Update windows: bundle model and runtime releases to match maintenance schedules, with rollback ready.
- Per-segment manifests: record exactly which versions run where; drift causes unrepeatable results.
- Capacity headroom: isolated estates cannot burst, so peaks must be planned.
Audit, scale and the business case
Audit in a telco is familiar territory: events flow to the SIEM, retention follows policy, and reviewers can reconstruct decisions. Air-gapped Plugsky supports this by exporting administrative, key and inference events into the same pipeline, so AI activity appears alongside network and security telemetry rather than in a separate silo.
Scale follows the segment-by-segment template: standardise the deployment, the evaluation suite and the update bundle, then replicate. Measure outcome quality, escalation rates and time saved per queue; those numbers justify expanding from a pilot to the wider estate.
Honest comparison
| Telco concern | Plugsky air-gapped | Cloud AI service | DIY serving build |
|---|---|---|---|
| Network isolation | No egress on the segment | Public endpoints | Your perimeter design |
| Per-segment fit | Independent runtimes and model sets | One shared service | You build each |
| Change control | Agents propose; change management executes | Not addressed | Your processes |
| Updates | Bundled to maintenance windows | Provider-scheduled | Your own |
| Audit | Events into existing SIEM | Vendor dashboards | Your pipelines |
| Effort | Deployment and pipeline | Lowest | Highest |
Frequently asked questions
Can agents change network configuration?
No. Keep tools read-only and route any proposed change through existing change management. Agents summarise, diagnose and propose; people and systems of record decide.
Do we need one deployment or several?
One per network segment. Separate runtimes, model sets and audit paths match existing boundaries and avoid forcing a single size on every workload.
How do updates align with maintenance windows?
Package model and runtime changes as verified bundles, validate on a staging mirror, then install during scheduled windows with a tested rollback path.
How do we keep subscriber data inside?
Inference, retrieval and logs stay in the perimeter. Minimise identifiers before prompting as defence in depth, and configure retention to policy.
What does the business case look like?
Measure outcome quality, escalation rate and handling time per queue against manual baselines. Start with one queue where the comparison is clean.
Can we mix local and hosted tiers?
Yes. Many operators keep documentation and engineering workloads hosted or in a VPC while subscriber and network workloads run air-gapped, all through the same API.
How do we start?
Pick one NOC queue or documentation corpus, deploy to the isolated staging segment, validate with operations staff, then expand segment by segment.