Key facts
| Embeddings | Live embeddings API for help-centre chunks with metadata filters |
| Generation | OpenAI-compatible chat completions with streaming and JSON mode (live) |
| Pipeline | Ingest, chunk, embed, permission-filter, retrieve, answer with citations |
| Models | 30+ models; cheap tiers for FAQ retrieval answers, stronger tiers for complex cases |
| Freshness | Store document versions with vectors so stale articles can be expired |
| Pricing | Flat monthly self-serve plans with no per-token charges on self-serve |
| Free tier | plugsky-micro and plugsky-lite on the free plan, no card required |
| Roadmap | Files and batch endpoints are coming soon; ingest documents in your own pipeline today |
TL;DR
- Retrieve from help centre, release notes and past tickets before answering.
- Filter by product version and entitlements, not just tenant.
- Cite the article or ticket behind every answer so agents can verify it.
- Deflect repeat questions; escalate with evidence instead of guessing.
- Re-index on content changes and measure answer accuracy monthly.
How it works, step by step
- Collect the knowledge sources: help articles, macros, release notes and resolved tickets with their resolutions.
- Chunk articles on headings and ticket threads on turns, and attach product, version and visibility metadata.
- Embed chunks with plugsky-embed and store vectors plus source links in your database.
- At question time, embed the customer message, filter by entitlement and product, and retrieve top candidates.
- Pass the passages to a chat model with an answer-only-from-context rule and a citation format.
- Add a confidence check: when retrieved evidence is weak, route to a human with the citations attached.
- Track deflection rate, citation accuracy and reopened tickets, then tune chunking and top-k.
Try it yourself
Open the best-model-for-RAG selector →
Retrieve first, answer second
Support answers live in your documentation, and documentation drifts. The agent's job is to find the current, correct passage and present it, not to recall policy from training data. Ingest help articles, macros, release notes and resolved tickets; chunk them so each unit is a coherent instruction or resolution; embed with plugsky-embed; and store the source link with every vector so answers can cite their origin.
Retrieval quality is decided before the model speaks. Keep product, version and visibility in metadata so a customer on an old plan never sees instructions for the new one. Deduplicate near-identical articles during ingestion, because redundancy crowds the prompt with the same advice three times.
Ticket context and citations
The customer's own history is part of the context: plan, products owned, recent tickets and the conversation so far. Assemble it compactly, then let retrieval supply the procedural knowledge. Ask for citations in a structured shape — JSON mode works well — so the interface can render links next to each claim.
- Resolve citations to real URLs or ticket ids before showing them; broken references erode trust.
- Store the document version with each chunk so answers can be traced to what was live at the time.
- Keep the last few turns verbatim and summarize older history instead of resending it all.
Deflection, escalation and freshness
A useful support agent does two things well: it resolves the repeatable questions instantly, and it exits gracefully when the question needs a person. Track deflection by intent rather than as a single number, because a high aggregate can hide poor performance on billing or security questions where errors are expensive.
Schedule re-indexing when content changes and expire vectors for retired articles, otherwise the agent will confidently quote last year's policy. Evaluate monthly on a set of real questions with known answers, checking citation accuracy and refusal quality alongside resolution. The free plan with plugsky-micro and plugsky-lite is enough to build the pipeline; the 14-day full-access trial lets you test stronger tiers on your hardest tickets, and the live pricing page covers paid options.
Honest comparison
| Concern | RAG support agent on Plugsky | Prompt-only chatbot | Human-only support |
|---|---|---|---|
| Answer source | Retrieved help articles and tickets | Model memory | Agent knowledge |
| Citations | Structured links to sources | None | Clickable docs in replies |
| Freshness | Re-index on content change | Stale until retrained | Training dependent |
| Entitlement safety | Metadata filters before ranking | Not enforceable | Manual checks |
| Cost profile | Cheap tiers for common questions | Unbounded confident answers | Headcount |
Frequently asked questions
What sources should a support RAG agent use?
Your help centre, macros, release notes and resolved tickets with their resolutions. Chunk each source so a unit expresses one instruction or fix, and keep the source link in metadata for citations.
How do I keep answers current?
Re-index when content changes and store document versions with vectors so retired articles can be expired. Stale vectors are the most common cause of confidently wrong support answers.
Should retrieval respect customer entitlements?
Yes. Filter by product, version, plan and visibility before ranking, so a customer never receives instructions for a feature they cannot use.
Which embedding model should I use?
plugsky-embed for English-heavy help centres and plugsky-embed-multilingual when content or customers mix languages. Embed documents and questions with the same model.
How does the agent know when to escalate?
When retrieval returns weak evidence, when the question matches a sensitive category, or when the customer asks for a person. Escalate with the retrieved citations attached.
Can answers include links?
Yes. Request a structured output mapping claims to chunk ids, resolve those ids to real URLs in your application, and render them beside the answer.
How do I measure success?
Track deflection per intent, citation accuracy, reopened tickets and refusal quality on a set of real questions, and re-run after every content or prompt change.
Can I try it for free?
Yes. plugsky-micro and plugsky-lite are on the free plan with no card, and a 14-day full-access trial is available for stronger models.