All use casesUse Case
Ground every CRM interaction in your actual customer data.
An AI CRM feature needs to retrieve the right customer context (deals, notes, call transcripts) before generating anything useful. The kit's RAG pipeline and per-rep API key tracking are built for exactly this shape of problem.
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The usual pain points
- ✕Indexing and retrieving the right customer record context per query
- ✕Keeping AI-generated summaries and suggestions grounded in real data, not hallucinated
- ✕Tracking usage per sales rep or team for internal cost allocation
- ✕Syncing CRM data updates into the retrieval index without a full re-index
How the kit solves them
- RAG pipeline with incremental ingestion keeps the index current as records change
- Per-API-key tracking maps naturally onto per-rep or per-team usage reporting
- System prompt configuration keeps generated summaries scoped to retrieved context
- Webhook endpoints ready to receive CRM change events for incremental re-indexing
Example implementation
@router.post("/v1/crm/summarize-account")
@require_api_key(tier=["sales-rep"])
async def summarize_account(body: AccountSummaryRequest, key: APIKey = Depends(get_api_key)):
context = await rag.query(question=f"Recent activity for {body.account_id}", collection="crm", top_k=5)
summary = await llm.chat(messages=build_summary_prompt(context), track_tokens=True)
await meter.record(key.id, summary.tokens)
return {"summary": summary.content, "sources": context.sources}Ready to build your ai-powered crm backend?
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