PromptShield AI – An AI Cost & Risk Firewall Built with Xano

You are an expert backend architect building a production-ready, multi-tenant AI infrastructure backend using Xano.

Build a backend called "PromptShield AI" — an AI Cost & Risk Firewall that sits between applications and multiple LLM providers (OpenAI, Anthropic, etc.) to enforce budgets, rate limits, and safety policies before requests reach the LLM.

The backend must be secure, scalable, and suitable for public API consumption.


Create the initial backend for PromptShield AI with the following requirements:

1. Core Concept
PromptShield AI acts as a proxy API for LLM calls. Applications send standard chat/completion payloads to PromptShield, which enforces usage policies, budgets, and risk checks before forwarding requests to LLM providers.

2. Database Schema (Postgres)
Design tables for:
- tenants (org_id, name, plan, created_at)
- api_keys (key, tenant_id, status, last_used_at)
- users (user_id, tenant_id, role)
- llm_providers (provider, model, cost_per_1k_tokens)
- usage_logs (tenant_id, user_id, feature, provider, model, tokens_in, tokens_out, cost, timestamp)
- budgets (tenant_id, scope_type [tenant/user/feature], scope_id, daily_limit, monthly_limit)
- policies (tenant_id, preferred_models, fallback_model, blocked_categories)

3. API Endpoints
Create the following APIs:

POST /llm/proxy
- Accepts OpenAI-compatible chat/completion payload
- Authenticates using API key
- Identifies tenant, user, and feature
- Performs budget checks and policy enforcement
- Routes request to the selected LLM provider
- Logs token usage and cost

POST /limits/configure
- Allows tenants to define per-user, per-feature, or per-tenant budgets
- Supports daily and monthly limits

GET /usage/summary
- Returns aggregated usage by tenant, user, feature, and model
- Optimized for dashboards

4. AI Logic
Use AI workflows to:
- Classify prompts for risky categories (PII, secrets, unsafe content)
- Block or redact requests that violate policy
- Automatically downgrade to cheaper models when nearing budget limits
- Detect anomalous usage spikes (e.g., sudden 10x increase)

5. Background Jobs
- Aggregate daily and monthly usage
- Recalculate remaining budgets
- Run anomaly detection periodically

6. Security & Scalability
- Multi-tenant isolation
- Rate limiting per API key
- Clean error responses
- Extensible provider abstraction

7. Output
Generate:
- Database tables
- API endpoint logic
- AI workflows
- Background jobs
Use clean, maintainable naming and comments suitable for a production backend.

Do NOT generate frontend code.
Focus entirely on the backend implementation in Xano.

## 🛠️ How I Refined the AI-Generated Backend in Xano

AI gave me a starting point — **human refinement made it production-ready**.

### Key Improvements I Made in Xano

#### 🔐 Security & Multi-Tenancy
- Introduced tenant isolation across all tables
- Added API key lifecycle management (create, revoke, rotate)
- Hardened error handling and rate limits

#### 💰 Cost & Budget Enforcement
- Added scoped budgets (tenant / user / feature)
- Implemented background aggregation jobs for daily & monthly usage
- Enabled budget thresholds and warning states

#### 🧠 AI Logic Enhancements
- Added prompt classification for risky categories (PII, secrets)
- Implemented policy-based model fallback when budgets are exceeded
- Designed provider abstraction for future expansion

#### 📊 Observability & Analytics
- Normalized usage logs for dashboards
- Enabled cost-by-model and cost-by-feature views
- Optimized APIs for frontend consumption