Where this offering fits
Paladin describes Intelliview as a conversational platform for big-data intelligence. Its evaluation should focus on whether users can ask relevant questions and verify the answers against authorised information. A fluent answer is not enough; the proposed workflow needs evidence, permissions and a clear distinction between retrieved information and inference.
Architecture and rollout decisions
Choose approved source collections and representative questions, including questions the available data cannot answer. Check how permissions limit the information visible to different reviewers. Establish the process for handling ambiguity, unsupported conclusions and corrections. Avoid loading sensitive business or personal records into an exploratory demo before processing and access arrangements are approved. The handover should specify who curates sources and who reviews output quality after the initial evaluation.
An evaluation scenario for this offering
Prepare questions with known answers, questions requiring several records and questions the approved source collection cannot answer. Review the response together with the underlying evidence and permissions. A restricted user should not gain information simply by changing the phrasing of a question. Record how unsupported or uncertain answers are recognised. This tests the actual information workflow rather than rewarding fluent responses that a reviewer cannot substantiate from the authorised material.
Evidence to request for this scope
- Obtain supported source and access-control details for the proposed Intelliview configuration.
- Test answer traceability with both permitted and deliberately unanswerable questions.
- Record how source updates and removal requests affect later answers and retained results.