Enterprise AI
Enterprise AI Integration Layer
A governed API layer that exposes AI capabilities to enterprise CRM and marketing platforms.
1 governed
Entry points
Policy middleware
Controls
Swappable
Providers
Architecture
Hover or tap any stage to see its role in the pipeline.
Data flow
Enterprise clients call the gateway with a signed request, policy middleware authenticates, redacts and rate-limits, the request routes to the appropriate AI service or provider, and prompts, responses, tokens and latency are logged for audit and cost reporting.
Problem statement
AI capabilities existed in isolated prototypes with no safe, consistent way for enterprise platforms to consume them.
Business objective
Provide one governed entry point for AI features, with authentication, rate limits, cost controls, logging and guardrails applied uniformly.
Solution overview
A FastAPI gateway in front of model providers and internal AI services, with policy middleware, prompt/response logging, PII redaction and per-consumer quotas.
AI / ML approach
Contract-first API design, provider abstraction, centralised guardrails, and observability on tokens, latency and failure modes.
Model / LLM used
Provider-agnostic LLM routing
Technology stack
PythonFastAPIREST APIsMicroservicesDockerAzureSalesforce
Key capabilities
- Single governed AI entry point
- PII redaction and content guardrails
- Per-consumer quotas and cost attribution
- Provider failover
- Full request/response audit trail
Evaluation approach
- Guardrail test suite for prompt injection and unsafe output
- Load testing against latency and quota policy
- Cost-per-consumer reporting accuracy
Challenges
- Prompt injection risk
- Runaway token cost
- Provider outages
Solution decisions
- Guardrails enforced at the gateway, not per application
- Hard token budgets per consumer with graceful degradation
- Provider abstraction so failover requires no client change
Business impact
Made AI a governed, reusable enterprise capability instead of a set of ungoverned point solutions.