1. Evaluate production delivery, not demos
Ask for examples where the partner moved from prototype to production with measurable business impact, monitoring, and rollback paths.
2. Verify data and integration competence
AI success depends on data quality, governance, and integration depth. Your partner should clearly explain ingestion, validation, and access controls.
3. Demand measurable KPIs before build starts
- Response quality targets
- Cycle-time reduction targets
- Support deflection or conversion impact
- Cost and latency thresholds
4. Review architecture and risk controls
Check for human-in-loop workflows, fallback paths, prompt/version control, and security model documentation.
5. Prefer phased delivery with explicit checkpoints
Discovery -> pilot -> production rollout is usually safer than one large delivery cycle.
Why teams pick UPNyX
- Delivery maturity across web, AI, and automation engineering.
- Clear architecture + KPI-led execution.
- Kerala-based execution with India-wide delivery capability.