The first instinct when shipping an internal assistant is to wire a vector store to a model and call it done. The hard part shows up later: spend growing without limit, answers leaking across teams that shouldn’t see each other’s context, and no clear record of who asked what.
SchneeAI’s value here is the control layer. Every request carries a service, tenant, and user identity — so budget counters know which team to charge, the cache knows which scope to read from, and audit events tie every prompt back to a real person. Your RAG layer focuses on retrieval quality. SchneeAI focuses on the parts that make the assistant safe to operate at company scale.
What you ship
- A grounded chat assistant — answers sourced from your internal docs, with the source chunks visible in the UI.
- Team-aware limits — monthly token budgets per team or department, with graceful degradation when the budget is exhausted.
- Per-team analytics — dashboards showing usage, cost, and popular queries by team, without cross-team exposure.
What SchneeAI handles
| Concern | Platform support |
|---|---|
| Tenant isolation | Per-team scope enforced across cache, Vault, and logs |
| Budget enforcement | Counters per team, feature, or service with threshold actions |
| Cost attribution | Provider cost captured at micro-USD precision, tied to the caller |
| Routing | Pick models by feature — cheap models for digests, premium for synthesis |
| Audit | Every prompt and response tied to a user, team, and timestamp |
| Prompt versioning | Update the system prompt without redeploying the assistant |
How it fits
A user in the Finance team asks a question. Your retrieval layer pulls candidate chunks from the Finance-scoped index. Your backend assembles the prompt — system, retrieved context, user message — and calls the Gateway. SchneeAI checks the Finance team’s budget, resolves the active prompt version, routes to the configured model, records the call, and returns the response. The response shows up in the UI with attribution.
If a user in Marketing asks the same question, they hit a different retrieval index, a different budget, and a different audit scope. The model call is identical. The governance around it is not.