A good AI pilot should be designed so you can harden it into production without a rewrite.
Reference building blocks
- API layer (authentication, rate limiting)
- Orchestration (workflows, queues, retries)
- Knowledge sources (documents, data warehouse)
- LLM/AI services (with logging and redaction)
- Evaluation (quality metrics, test sets)
- Observability (traces, dashboards, alerts)
What usually breaks pilots
- missing baseline and measurement
- weak exception handling
- unclear permissions over documents
- no monitoring of quality drift
If you want a 30-day pilot that includes measurement and hardening, start here: AI implementation (30/60/90 days).
Related:
- RAG decision criteria: What is RAG and when it makes sense
- document extraction patterns: Document intelligence
Want a similar setup? See case study: MyZenCheck and book a call via contact.