FAQ
Domande prima dell'implementazione
Risposte brevi sul lavoro del servizio AI, sui contenuti CMS e sulla preparazione alla produzione.
What does Baciu.com build?
We design and implement production AI systems: agentic workflows, reasoning services, retrieval, and automation connected to operational tools.
Is this a product or a services practice?
This is an expert services practice with reusable engineering patterns. Delivery is adapted to each client's process, data, and governance model.
Where does Payload CMS fit?
Payload powers learn.baciu.com content and can drive editable site content for controlled publishing workflows.
Do you support multilingual content workflows?
Yes. Content can be routed across all supported locales with translation status tracking and staged review before publication.
How do you handle sensitive data in AI systems?
We enforce least-privilege access, permission-aware retrieval, audited tool use, and environment separation across pilot and production.
What is included in a typical pilot?
A scoped workflow, integration boundaries, evaluation plan, escalation logic, and a measurable go/no-go recommendation.
How do you measure quality beyond accuracy?
We track factuality, source coverage, latency, cost, refusal behavior, escalation rates, and downstream resolution outcomes.
Can your systems integrate with ticketing and ERP tools?
Yes. We implement typed tool interfaces with idempotency, retry strategy, and audit-friendly action traces.
Do you provide post-launch support?
Yes. We provide stabilization, operating cadence support, and governance updates after launch.
How does human approval work in automated flows?
Approval policies are risk-based and action-specific. Reviewers receive evidence packets with context, confidence, and proposed actions.
What if model behavior drifts over time?
We define monitoring and re-evaluation loops, with remediation runbooks and rollback procedures tied to release governance.
Can we start with one department and scale later?
Yes. Most programs begin with one queue or workflow, then scale via reusable patterns once quality and ownership are proven.
How do you estimate AI ROI before production data exists?
Start with current volume, handling time, error rates, escalation share, wait time, and labor mix. Then model conservative adoption over staged rollout instead of assuming full automation on day one.