Key facts
| API surface | OpenAI-compatible /v1/chat/completions; keep your existing SDK |
| Grounding | Embeddings and RAG are live for design, live-ops and support knowledge |
| Models | 30+ models behind one API, from free tiers to frontier |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Access control | Scoped API keys, rotation and usage analytics; enterprise SSO and RBAC options |
| Endpoint roadmap | Audio, images, moderation, batch and fine-tuning are coming soon |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Free tier | Free plan with plugsky-micro and plugsky-lite; 14-day full-access trial |
TL;DR
- Ground gaming answers in approved internal content with RAG instead of model memory.
- Keep unreleased or player content inside VPC, on-prem or air-gapped deployments where policy requires it.
- Scope retrieval per team, client or site so permissions and confidentiality hold at query time.
- Keep a named human owner for every player-facing decision.
- Exclude unreleased content from general retrieval.
How it works, step by step
- Inventory the content to make searchable: design documents, live-ops runbooks, localization glossaries and player-support macros.
- Define a data boundary for the pilot that excludes restricted material until controls are proven.
- Choose a deployment target: cloud for public content, VPC, on-prem or air-gapped for restricted data.
- Build an evaluation set with live-ops leads and community managers so answer quality is judged by domain experts.
- Ingest, chunk and embed approved documents, and require citations on every answer.
- Add refusal behavior for questions outside the indexed, approved content.
- Review logged interactions on a schedule and expand only after accuracy and access checks pass.
Original data
Try it yourself
Where gaming teams start
Start with internal questions that already have a written answer. High-value first workloads include:
- Design and systems docs: retrieve design intent, formulas and tuning notes.
- Live-ops runbooks: answer from event, incident and deployment procedures.
- Player support macros: draft replies from policy, refund and account content.
- Localization glossaries: keep terminology consistent across languages and regions.
Each use case augments staff with cited answers; none replaces producer judgment.
A private RAG architecture for gaming knowledge
The stack is consistent across industries: ingest approved design documents, live-ops runbooks, localization glossaries and player-support macros, chunk and embed with a multilingual embedding model, store vectors inside your environment, and call chat completions that answer only from retrieved context. Plugsky embeddings and chat completions are OpenAI-compatible, so teams already using OpenAI SDKs change the base URL and keep their code.
Tag content by project, release status and region, and exclude embargoed material from general retrieval. Use metadata filters for platform and locale, and keep a reranker for long design documents and patch notes.
Access control, confidentiality and audit
Unreleased content, player data and moderation records are the sensitive classes. Scope retrieval by project and role, keep embargoed material out of general indexes, and keep humans responsible for bans, refunds and any player-facing message.
The technical controls are consistent: enforce permission-aware retrieval in your own service layer, scope API keys per application, team or tenant, rotate keys, and retain request and response logs on a defined schedule. Regulatory obligations vary by jurisdiction and sector, so map them with counsel rather than assuming one framework covers every deployment; Plugsky supplies the deployment and logging primitives you document.
See private AI for related deployment and control detail.
Rollout and human oversight
Pilot on published support and policy content before touching unreleased or player content. Build a labelled question set with live-ops leads and community managers, then measure retrieval hit rate, citation correctness and answer accuracy before and after every index or model change. Require citations on every answer, refuse out-of-scope questions, and name a human owner for every player-facing decision.
Review logged interactions weekly at first, correct the index rather than the prompt when retrieval misses, and expand the corpus only when accuracy and access checks pass.
Honest comparison
| Capability | Plugsky | Public AI assistants | Building in-house |
|---|---|---|---|
| Data boundary | Cloud, VPC, on-prem or air-gapped | Vendor cloud only | You control fully |
| Grounding | Embeddings and RAG are live for design, live-ops and support knowledge | Uncontrolled retrieval | You assemble and operate |
| Access control | Scoped keys, usage analytics, enterprise SSO and RBAC options | Account-level only | Custom identity work |
| Auditability | Request and response logging | Limited | You build logging |
| Pricing | Flat monthly self-serve plans; see live pricing | Per-seat or per-token | GPU plus operations cost |
| Time to pilot | Days | Hours, without residency control | Quarters |
Frequently asked questions
Can game studios keep data private with Plugsky?
Yes. Choose the deployment boundary that matches the data: Plugsky cloud for public content, or your VPC, on-prem and air-gapped options for restricted material. Access is controlled with scoped API keys and usage analytics.
Do we need to fine-tune on our internal documents?
Not for a first release. Fine-tuning is coming soon and is better for style than facts. RAG keeps answers current, permission-aware and traceable to a source, which matters more for internal knowledge.
Which model should we use?
Start free with plugsky-micro and plugsky-lite to validate retrieval, then evaluate mid-tier and frontier models from the 30+ model catalogue on your own question set.
How do we stop wrong or unsupported answers?
Restrict the assistant to approved indexed content, require citations, refuse out-of-scope questions and keep a human decision-maker for every regulated or client-facing outcome.
How is pricing structured?
Self-serve plans are flat monthly with unlimited fair-use usage, and there are no per-token charges on self-serve plans. See the live pricing page for current plans and the free tier.
Can it moderate player content?
The moderation endpoint is coming soon. Today, use RAG to retrieve policy and guide human moderators, and keep every enforcement decision with a person.
How do we protect unreleased game content?
Keep unreleased material in a scoped workspace excluded from general retrieval, and tag chunks by release status so embargoes are enforced.