All use casesUse Case
Draft, triage, and respond to email at scale with an LLM backend.
An email assistant needs context retrieval (past threads, CRM data), LLM drafting, and usage tracking per mailbox or seat. This maps directly onto the kit's RAG pipeline, unified LLM layer, and per-API-key billing.
FastAPIOpenAIpgvectorCeleryPostgreSQLStripe
The usual pain points
- ✕Grounding drafted replies in prior thread context and CRM data
- ✕Triaging and prioritizing incoming email without manual review
- ✕Billing per seat or per mailbox rather than per raw API call
- ✕Processing inbound email webhooks without blocking the request
How the kit solves them
- RAG pipeline indexes prior threads for context-grounded drafts
- Unified LLM layer handles drafting, summarizing, and triage classification
- Stripe metering supports per-seat billing tiers out of the box
- Celery background jobs process inbound webhook payloads asynchronously
Example implementation
@router.post("/v1/email/draft-reply")
@require_api_key(tier=["seat"])
async def draft_reply(body: DraftReplyRequest, key: APIKey = Depends(get_api_key)):
context = await rag.query(question=body.thread_summary, collection=f"mailbox-{key.id}", top_k=3)
draft = await llm.chat(messages=build_reply_prompt(body, context), track_tokens=True)
await meter.record(key.id, response.tokens)
return {"draft": draft.content}Ready to build your ai email assistant backend?
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