Sinequa

Open the official Sinequa product or documentation and begin with one bounded workflow that matches its core purpose: enterprise AI search platform for finding and synthesizing information across large private knowledge collections. Use representative material from the environment where you expect to deploy it rather than relying only on sample data.

Connect or upload the minimum data required for the test. Keep credentials and sensitive datasets protected, and confirm that user permissions are preserved when enterprise knowledge or customer systems are involved. For financial workflows, retain original source documents. For sales and contact-center tools, define clear escalation rules and restrict autonomous actions until behavior is understood.

Run several normal and difficult cases. Compare extracted fields, generated answers, recommendations, or conversation insights against trusted references. Track accuracy, latency, missing context, false positives, and the amount of manual correction still required. If the product takes actions, verify idempotency, auditability, and approval controls for consequential steps.

Before scaling, review privacy, retention, security documentation, integration limits, observability, and expected cost. The catalog currently classifies pricing as contact, but current plans and billing terms should be confirmed directly with the provider.