Orchestrating deep research with Temporal, OpenAI, and Box AI

A demo workflow for an international film distributor combines OpenAI, Temporal, Box, and Box AI to research how audiences discover independent films in France, Japan, Brazil, and Poland, emphasizing multilingual retrieval so local-language evidence is not missed. OpenAI creates market-specific search plans, gathers current web sources, preserves citations, synthesizes a shared cross-market report, and generates localized briefs, while Temporal manages dependencies, concurrent searches, retries, partial failures, and recoverable execution state. Typed search results and a shared evidence base help ensure that localization does not alter underlying findings or obscure uncertainty. Research artifacts are published to Box using stable workflow IDs and file versioning to reduce duplicate uploads during retries, although the implementation is retry-safe rather than fully idempotent. Box AI then compares the external report with explicitly selected internal files or a Box Hub, respecting existing permissions and clearly separating public research from proprietary context. The open-source demonstration illustrates how durable orchestration, multilingual evidence collection, governed content storage, and internal-context analysis can support auditable enterprise research workflows.

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